JP2009180051A - Method, apparatus, and system for managing and supporting rainwater inflow facility and program functioning as the apparatus - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To stably and accurately estimate changes in the quantity of flow or water quality in rainwater inflow facilities over a long period. <P>SOLUTION: In a method for managing and supporting rainwater inflow facilities, the quantity of flow or water quantity of sewage is estimated on the basis of the electric conductivity of the sewage in the rainwater inflow facilities. A dilution ratio is computed by dividing the electric conductivity in clear weather observed by a conductivity meter 12 installed to the rainwater inflow facilities by an electric conductivity value of sewage in rainy weather observed by the conductivity meter 12. The quantity of flow of the sewage is estimated on the basis of the dilution ratio. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention provides a rainwater inflow facility management support for estimating a flow rate change or a water quality change in the rainwater inflow facility in order to appropriately manage the operation of the rainwater inflow facility such as a water drainage chamber or a waterhole connected to the basin sewer during rain The present invention relates to a method, a rainwater inflow facility management support device, a rainwater inflow facility management support system, and a program that functions as a rainwater inflow facility management support device.

  In general, in rainwater inflow facilities such as combined sewers, pollutants in water discharged into public water areas such as rivers, lakes and marshes in rainy weather have an adverse effect on the water environment of public water. In particular, overflowing water from sewage treatment plants is a major problem because of the large inflow of rainwater in combined sewers. In response to these problems, the Sewerage Enforcement Ordinance was amended to obligate observation of the overflow situation from rainwater inflow facilities such as pump stations, sewage treatment plants, rainwater storage facilities, and natural spouts.

  However, this observation is obliged to be performed once a year, and is not practical when various rainfall characteristics are considered. Therefore, it is necessary to continuously observe the overflow situation in order to further improve the practicality. In particular, it is required to observe the water quality during rainy weather because the pollutant accumulated in the combined sewer flows out during rainy weather.

  Along with this, the introduction of a water quality prediction system that predicts the water quality in the rainwater inflow facility during rainy weather is requested, and appropriate operation management of the rainwater inflow facility is performed based on the characteristics of the water quality in the rainwater inflow facility. It is also requested to do.

  In Patent Document 1, the turbidity of a rainwater treatment facility is measured by a turbidimeter, and the difference between the measured turbidity and the predicted turbidity of a rainwater inflow facility predicted by a water quality prediction unit is corrected to turbidity. A rainwater inflow facility management support apparatus that predicts the water quality of a rainwater inflow facility based on the turbidity correction value and controls the water quality of the rainwater inflow facility is disclosed.

JP 2006-274577 A (abstract, detailed description of the invention)

  However, in the rainwater inflow facility management support device disclosed in Patent Document 1, a water quality change is predicted using a turbidimeter. This turbidimeter has a problem that deposits such as algae are easily attached and stable water quality cannot be continuously observed. For this reason, the rainwater inflow facility management support device disclosed in Patent Document 1 has a problem that it is impossible to predict a change in water quality for a long period of time.

  It is also possible to install a flow rate observation device in the rainwater inflow facility and predict a change in the water quality of the rainwater inflow facility based on the flow rate observed by the flow rate observation device. However, since the flow rate observation device needs to observe the flow rate at a rectified place, it cannot observe an accurate flow rate at a place where the flow of water changes like a combined sewer. Thus, since the place where flow rate observation is performed is restricted, the place where water quality change is predicted is also restricted, and as a result, there is a problem that proper operation management of the rainwater inflow facility cannot be performed.

  The present invention has been made based on the above circumstances, and its purpose is to perform a long-term, stable and stable flow rate change or water quality change in the rainwater inflow facility in order to perform appropriate operation management of the rainwater inflow facility. A rainwater inflow facility management support method, a rainwater inflow facility management support device, a rainwater inflow facility management support system, and a program that functions as a rainwater inflow facility management support device that can be accurately estimated are provided.

  In order to solve the above problems, the present invention is a rainwater inflow facility management support method for estimating the flow rate or quality of sewage from the conductivity of sewage in the rainwater inflow facility, the conductivity installed in the rainwater inflow facility. Calculate the dilution rate by dividing the conductivity value of sewage on clear days observed by the meter by the conductivity value of sewage on rainy days observed by the conductivity meter, and estimate the sewage flow rate based on the dilution rate The rainwater inflow facility management support method is characterized by

  When such a method is used, the sewage on a rainy day is compared to the sewage on a sunny day by calculating the dilution rate by dividing the conductivity value of the sewage on a clear day by the conductivity value of the sewage on a rainy day. You can ask how much it was diluted. Here, sewage means that rainwater is mixed into sewage. For this reason, it becomes possible to estimate the flow rate on rainy days based on the dilution rate. Therefore, the flow rate is estimated using a conductivity meter, and as a result, compared to the case where a flow meter is used, the flow rate is estimated long-term, stably and accurately without being restricted by the observation location. Can do.

  The present invention further stores past conductivity observation data, which is time series data of a plurality of conductivity values observed in the past at a rainwater inflow facility, in a conductivity observation data storage unit, and estimates the estimated date by a conductivity meter. Estimated daily conductivity observation data, which is time series data of conductivity values observed in sewage, is stored in the observation value storage unit, and the estimated daily conductivity observation data is extracted from the observation value storage unit. The past conductivity observation data is extracted in order from the storage unit, and it is determined whether the past conductivity observation data extracted by the conductivity observation data extraction unit approximates the estimated daily conductivity observation data. Based on this, the stormwater inflow facility management support method predicts the future sewage flow rate.

  In this way, the flow of sewage in the future can be predicted by using a lot of past conductivity observation data.

  Further, according to the present invention, the above-described estimation / prediction of flow rate (meaning estimation or prediction) is performed using a model formula having a dilution rate as an input and a flow rate as an output. Management support method. For this reason, it becomes possible to estimate the flow rate of the current sewage or to predict the flow rate of the future sewage more accurately based on the dilution rate.

  The present invention further provides a rainwater inflow facility management support method characterized by estimating / predicting water quality based on the estimated / predicted flow rate. For this reason, it becomes possible to estimate / predict the flow rate of sewage based on the conductivity value, and further estimate / predict water quality.

  Further, the present invention is a rainwater inflow facility management support apparatus that estimates the flow rate or water quality of the sewage from the conductivity of the sewage in the rainwater inflow facility, the storage unit storing the calculated data, the rainwater inflow facility A conductivity observation data storage unit for storing past conductivity observation data, which is time-series data of a plurality of conductivity values observed in the past, and conductivity values of sewage observed on an estimation date by a conductivity meter. An observation value storage unit for storing a certain estimated day conductivity observation value, and a sunny day day conductivity observation data extraction unit for extracting past conductivity observation data of sewage observed on a clear day from the conductivity observation data storage unit; Dilution rate calculation unit that calculates the dilution rate by dividing the value of past conductivity observation data of sewage on a clear day by the estimated daily conductivity value of sewage, and the flow rate value of sewage to be estimated based on the dilution rate Calculate the flow value We have a constant flow rate value calculation unit, and the rainwater inflow facility management support apparatus having a.

  For this reason, the present sewage flow rate can be estimated based on the conductivity value observed on the estimation date. The conductivity can be observed without being constrained by the location as compared with the case of directly observing the flow rate. For this reason, it is possible to estimate the flow rate in a long term, stably and accurately without being restricted by the observation location.

  Further, the present invention further includes a storage unit for storing calculated data, and a conductivity for storing past conductivity observation data that is time series data of a plurality of conductivity values observed in the past in a rainwater inflow facility. Estimated from the observed value storage unit, the observed value storage unit that stores the estimated daily conductivity observation data that is the time series data of the conductivity value of the sewage observed on the estimated date by the conductivity meter, and the estimated value storage unit Conductivity observation data extracted from the conductivity observation data storage unit and the conductivity observation data extraction unit for extracting the past conductivity observation data in order from the conductivity observation data storage unit and the past conductivity observation extracted by the conductivity observation data extraction unit Approximate discriminating unit that discriminates whether or not the data approximates the estimated daily conductivity observation data, and the conductivity value of the future sewage from the past conductivity observation data discriminated to be approximate by the approximate discriminating unit A sewage inflow facility management characterized in that the estimated flow rate value calculation unit calculates the dilution rate from a future sewage conductivity value and calculates a flow rate value to be predicted. It is a support device.

  For this reason, the flow volume of the future sewage can be estimated by using many past conductivity observation data.

  In the rainwater inflow facility according to the present invention, the approximate determination unit further determines whether or not the estimated daily conductivity observation data and the past conductivity observation data are approximated in a past common time series. It is a management support device. As described above, since the approximate discrimination between the estimated daily conductivity observation data and the past conductivity observation data is performed in a common time series, it is possible to improve the discrimination accuracy.

  The present invention further includes a cumulative square error calculation unit that calculates a cumulative square error between the estimated daily conductivity observation data and the past conductivity observation data extracted by the conductivity observation data extraction unit. The determination of the approximation by the determination unit is performed based on the accumulated square error calculated by the accumulated square error calculation unit, and the rainwater inflow facility management support device is provided. For this reason, approximate discrimination can be performed with high accuracy. Therefore, it is possible to improve the discrimination accuracy.

  Further, according to the present invention, the approximation determining unit further causes the storage unit to store data relating to the upper one or higher plurality of past conductivity observation data although the accumulated square error calculated by the accumulated square error calculating unit is small. It features a rainwater inflow facility management support device. In the case of such a configuration, a plurality of past conductivity observation data serving as data for estimating the flow rate can be secured, so that the discrimination accuracy can be improved.

  Further, the present invention is a rainwater inflow facility management support device further comprising a day-by-day conductivity observation data extraction unit for extracting past conductivity observation data on a clear day by day of the week. With this configuration, it is possible to acquire past conductivity observation data on a clear day in consideration of the observation day characteristics.

  Further, according to the present invention, the sunny day conductivity observation data extraction unit extracts a plurality of sunny day past conductivity observation data from the conductivity observation data storage unit, and the extracted plurality of sunny day past conductivity. The rainwater inflow facility management support device is characterized by calculating an average value of observation data. When configured in this way, it is possible to obtain more accurate sunny day conductivity observation data.

  Further, the present invention provides a rainwater inflow facility management support device characterized in that the flow rate value calculation in the estimated flow rate value calculation unit is performed using a model formula with the dilution rate as an input and the flow rate as an output. Yes. For this reason, it becomes possible to estimate the flow rate of the current sewage or to predict the flow rate of the future sewage more accurately based on the dilution rate.

  Furthermore, the present invention is a rainwater inflow facility management support device that further estimates / predicts the water quality based on the estimated / predicted flow rate value. For this reason, it becomes possible to estimate / predict the flow rate of sewage based on the conductivity value, and further estimate / predict water quality.

  In addition, the present invention is a rainwater inflow facility management support system that includes any of the above-described rainwater inflow facility management support devices and a conductivity meter that observes the conductivity of sewage or sewage in the rainwater inflow facility.

  When such a system is configured, it is possible to estimate the flow rate and water quality in a long-term, stable and accurate manner without being restricted by the observation location.

  In addition, the present invention is a program that causes a computer to function as any of the above-described rainwater inflow facility management support devices.

  In such a configuration, the program can be copied to a medium such as a disk, and the program copied to the medium can be installed on a plurality of computers. Therefore, it is possible to cause a plurality of computers installed with the program to function as a rainwater inflow facility management support apparatus.

  According to the present invention, it is possible to estimate a change in flow rate or a change in water quality in a rainwater inflow facility over a long period of time, stably and accurately.

  Hereinafter, a rainwater inflow facility management support system 10 according to an embodiment of the present invention will be described with reference to the drawings.

  Further, hereinafter, the rainwater inflow facility management support apparatus 20, the rainwater inflow facility management support method, and the program that causes the rainwater inflow facility management support apparatus 20 to function will be described together with the rainwater inflow facility management support system 10.

  FIG. 1 is a diagram showing a configuration of a rainwater inflow facility management support system 10 according to an embodiment of the present invention.

  The rainwater inflow facility management support system 10 observes the conductivity of rainwater in the rainy day in a rainwater inflow facility such as a rainwater discharge chamber, a basin sewer connection hole or a combined sewer, and the value of the observed conductivity ( This is a system that estimates / predicts the current or future flow rate and water quality changes over time on rainy days from EC values). Here, the estimation is used for the current flow rate or water quality, and the prediction is used for the future flow rate or water quality. BOD (Biochemical Oxygen Demand) value is adopted as an indicator of water quality. As shown in FIG. 1, the rainwater inflow facility management support system 10 includes a conductivity meter (hereinafter referred to as EC meter) 12 that observes the conductivity in water, and a rainwater inflow facility management support device 20. In the present embodiment, the conductivity in the rainwater inflow facility is observed by the EC meter 12 on a rainy Sunday, and the flow rate and water quality at the time of observation are estimated and observed based on the observed conductivity value. Based on the observed conductivity value for 30 minutes, the flow rate and water quality over time for 1 hour shall be predicted.

  The EC meter 12 is installed, for example, at a predetermined position in the combined sewer system to be estimated. The rainwater inflow facility management support device 20 estimates the flow rate and water quality at a predetermined time (current) on a rainy day based on the EC value observed by the EC meter 12, or changes the flow rate and water quality over time at the predetermined time. It is a prediction device.

  The rainwater inflow facility management support device 20 is configured by a computer system, and mainly includes a control unit 22, a calculation unit 24, a display unit 26, an EC observation value collection unit 28, an observation value storage unit 29, A storage unit 30, an EC observation data storage unit 32 that is a conductivity observation data storage unit, a rainy EC observation data storage unit 33 that is a rainy conductivity observation data storage unit, an EC estimation unit 34, and a flow rate estimation unit 36; The water quality estimation unit 38 is connected to each other by a bus. The calculation unit 24, the display unit 26, the EC observation value collection unit 28, the observation value storage unit 29, the storage unit 30, the EC observation data storage unit 32, the rainy weather EC observation data storage unit 33, the EC estimation unit 34, and the flow rate estimation unit 36 and the water quality estimation part 38 operate | move based on the instruction | command of the control part 22. FIG. By cooperating these hardware and software, it is possible to estimate the flow rate change and the water quality change by the rainwater inflow facility management support device 20. Here, each component can perform not only estimation but also prediction processing. The components (29, 30, 32, 33) that store data are mainly configured by a memory such as a RAM and a CPU. The control unit 22, the calculation unit 24, the EC observation value collection unit 28, the EC estimation unit 34, the flow rate estimation unit 36, and the water quality estimation unit 38 are mainly configured by a CPU. The display unit 26 is mainly configured by a CPU and a display.

  As described above, the control unit 22 is a part that controls the entire rainwater inflow facility management support apparatus 20. For example, the control unit 22 executes a program stored in the storage unit 30, receives data calculated by the calculation unit 24, the water quality estimation unit 38, and the like, processes the data, and then displays the data on the display unit 26 and the like. Send. The arithmetic unit 24 is a part that performs arithmetic processing based on input data. The display unit 26 is a part that outputs various input data. The EC observation value collection unit 28 is a part that periodically collects EC values observed by the EC meter 12, for example, every minute. The observation value storage unit 29 is estimated date conductivity observation data (hereinafter referred to as estimated date EC observation data), which is time-series data of EC values acquired by the EC observation value collection unit 28, or an estimated date. In addition, it is a part for storing time-series data of flow rate values and turbidity values observed by a flow meter and a turbidimeter (not shown). The storage unit 30 stores a calculation program and stores various calculated data as appropriate.

  The EC observation data storage unit 32 is a database that stores past conductivity observation data (hereinafter referred to as past EC observation data) that is time-series data of a plurality of EC values observed in the past in the rainwater inflow facility. is there. The EC observation data storage unit 32 stores past EC observation data, for example, in a state classified according to weather such as rainy days and sunny days, and days of the week. The rainy weather EC observation data storage unit 33 is a database that stores a plurality of past EC observation data of rainy days observed in the past at the rainwater inflow facility. The EC estimation unit 34 is a part that calculates an EC value (hereinafter referred to as an estimated EC value) estimated in the rainwater inflow facility to be estimated based on the estimated date EC observation data and the past EC observation data. The flow rate estimation unit 36 is a part that calculates a flow rate value (hereinafter referred to as an estimated flow rate value) estimated in the rainwater inflow facility based on time-series data of estimated EC values (hereinafter referred to as estimated EC data). is there. The water quality estimation unit 38 time-sequentially estimates BOD values (hereinafter referred to as estimated water quality values) in the rainwater inflow facility based on time-series data of estimated flow values (hereinafter referred to as estimated flow data). This is the part to calculate.

  The EC estimation unit 34 includes an EC observation data extraction unit 40 that is a conductivity observation data extraction unit, a cumulative square error calculation unit 42, an approximation determination unit 44, and an estimated EC value calculation unit 46 that is a conductivity value calculation unit. Have. Further, the flow rate estimation unit 36 includes a sunny day EC observation data extraction unit 48 that is a sunny day conductivity observation data extraction unit, a day-by-day EC observation data extraction unit 50 that is a day-by-day conductivity observation data extraction unit, and a dilution rate. A calculation unit 52 and an estimated flow value calculation unit 54 are included.

  The EC observation data extraction unit 40 extracts the estimated date EC observation data from the observation value storage unit 29 and, for example, from the past EC observation data stored in the EC observation data storage unit 32, for example, one past This is a part for sequentially extracting EC observation data. The cumulative square error calculation unit 42 is a part that calculates a later-described cumulative square error between the estimated date EC observation data and the past EC observation data in a predetermined time-series range from the past to the present. The approximate discriminating unit 44 stores associating data for associating the values of the top 10 cumulative square errors with a small cumulative square error, the accumulated square error values, and the past EC observation data used for the calculation thereof. It is a part to memorize. The estimated EC value calculation unit 46 is a part that calculates an estimated EC value based on the association data.

  The sunny day EC observation data extraction unit 48 is a part for extracting past EC observation data on a sunny day from the EC observation data storage unit 32. The EC observation data extraction unit 50 for each day of the week is a part for extracting the past EC observation data for Sunday, which is the estimated day of the week, from the past EC observation data for the clear sky extracted by the clear day EC observation data extraction unit 48. is there. The dilution rate calculation unit 52 is a part that acquires past EC observation data and estimated EC data on a sunny day, and calculates a dilution rate described later from the values of those data. The estimated flow value calculation unit 54 is a part that creates a flow rate estimation formula that is a model formula for calculating a flow rate based on the dilution rate, and calculates an estimated flow rate value from the created flow rate estimation formula.

  Next, processing in which the rainwater inflow facility management support system 10 estimates the flow rate and water quality at the time of observation based on the observed EC value will be described. The flow rate is estimated by the flow rate estimation unit 36 calculating an estimated flow rate value. The water quality is estimated by the water quality estimation unit 38 calculating the estimated water quality value. Note that these calculation processes are performed based on commands from the control unit 22.

  First, the calculation process of the estimated flow value in the flow estimation part 36 among the calculation processes of the rainwater inflow facility management support system 10 mentioned above is demonstrated. FIG. 2 is a flowchart showing processing for estimating the flow rate during observation based on the observed conductivity value by the flow rate estimation unit 36.

  As shown in FIG. 2, first, the sunny day EC observation data extraction unit 48 stores the rainy day past EC observation data stored in the rainy day EC observation data storage unit 33 and the EC observation data storage unit 32. The past EC observation data stored in the EC observation data storage unit 32 is identified. And the past EC observation data of the clear sky day identified from EC observation data memory | storage part 32 are extracted (step S101). Next, the day-by-day EC observation data extraction unit 50 further extracts Sunday past EC observation data from a plurality of past EC observation data on a fine day extracted in step S101 (step S102). Further, the day-by-day EC observation data extraction unit 50 calculates the average value of the past EC observation data on the Sunday on which the sunny day is extracted (hereinafter referred to as the clear day EC observation data) (step S103).

  Next, the dilution rate calculation unit 52 acquires the EC value observed by the EC meter 12 on the estimation date (step S104). Further, time-series data of EC observation values corresponding to the time of the EC value acquired in step S104 is extracted from the clear sky day EC observation data calculated in step S103 (step S105).

  Furthermore, the dilution rate calculation unit 52 uses the EC observation value (EC (dry)) on a clear day extracted in step S105 and the EC value (EC (wet)) on a rainy day acquired in step S104, as follows. The dilution rate Z is calculated by the equation (1) shown in (1).

Z = EC (dry) / EC (wet) (1)
Z: Dilution rate
EC (dry): EC observations on a clear day
EC (wet): EC observation value on rainy days

  Next, the estimated flow value calculation unit 54 creates a flow rate estimation formula represented by the following formula (2) (step S107). The flow rate estimation formula is a quadratic polynomial for calculating the estimated flow rate Q with the dilution rate Z as an input. Α, β and γ in the flow rate estimation formula are coefficients, and these values are determined based on the rainfall on the observation day. As the values of the coefficients α, β, and γ, the one having the correlation coefficient between the flow rate estimation formula and the observed flow rate value (flow rate observation value) closest to 1 using regression analysis is adopted. Note that the flow rate estimation formula is not limited to a quadratic polynomial, and other forms of formula such as a linear formula or a cubic formula can be appropriately used depending on the situation.

Q = αZ ² + βZ + γ (2)
Q: Estimated flow rate
Z: Dilution rate
α, β, γ: coefficients

  Further, the estimated flow value calculation unit 54 calculates the estimated flow value at the time of observation by substituting the value of the dilution rate Z calculated in step S106 into the flow rate estimation formula created in step S107 (step S108). ). Through the above processing, an estimated flow rate value at the time of observation is calculated based on the observed EC value. As a result, it is possible to estimate the flow rate during observation.

  Next, calculation processing of the estimated water quality value in the water quality estimation unit 38 among the calculation processing of the rainwater inflow facility management support system 10 described above will be described.

  The water quality estimation unit 38 first acquires the estimated flow rate value calculated by the flow rate estimation unit 36. Next, based on the estimated flow rate value, an estimated water quality value at the time of observation is calculated using known water quality estimation model formulas represented by the following formulas (3) and (4). The load runoff coefficient C, deposition load P and flow coefficient m in the equations (3) and (4) are parameters, and these values are determined based on empirical rules and rainfall on the observation day.

dP / dt = DWL-C · P · Q ^m (3)
L = C ・ P ・ Q ^m・ ・ ・ ・ ・ ・ ・ ・ ・ ・ ・ (4)
L: Load amount
C: Load outflow coefficient (parameter)
P: Deposition load (parameter)
Q: Estimated flow rate
m: Flow coefficient (parameter)
DWL: Sunny day load

  Through the above processing, the estimated water quality value at the time of observation is calculated from the estimated flow value calculated by the flow rate estimation unit 36. And the water quality at the time of observation can be estimated from the calculated estimated water quality value.

  Next, a process in which the rainwater inflow facility management support system 10 predicts temporal changes in the EC value, flow rate, and water quality for the next hour based on the observed conductivity values for 30 minutes will be described. The EC value is predicted by the EC estimation unit 34 calculating the EC value. The prediction of the flow rate is performed by the flow rate estimation unit 36 calculating the flow rate value. The water quality is predicted by the water quality estimation unit 38 calculating the water quality value. Note that these calculation processes are performed based on commands from the control unit 22.

  First, among the calculation processes of the rainwater inflow facility management support system 10 described above, the EC value calculation process in the EC estimation unit 34 will be described. Note that the number of past EC observation data stored in the EC observation data storage unit 32 is nd. The EC value calculation process in the EC estimation unit 34 is performed based on a command from the control unit 22.

  FIG. 3 is a flowchart showing an EC value calculation process for performing prediction for one hour after observation in the EC estimation unit 34.

  When the power supply of the rainwater inflow facility management support system 10 is input, the EC estimation unit 34 starts processing according to a command from the control unit 22. The storage unit 30 includes ten storage areas (M1 to M10) that store the value of the cumulative square error calculated by the cumulative square error calculation unit 42 described later. In the present embodiment, the number of storage areas (M1 to M10) is 10 from the viewpoints of EC value verification and quickness of calculation processing.

  As shown in FIG. 3, first, the control unit 22 assigns ∞ as an initial value to each of the storage areas (M1 to M10), for example. Also, the value of the cumulative square error calculation count (= I) is set to 0, and the value of the cumulative square error stored in the storage area (M1 to M10) is compared with the acquired calculated value (= T). The value of the repetition variable (= n) used at this time is set to 1 (step S201).

  Next, the EC observation data extraction unit 40 extracts the estimated date EC observation data from the observation value storage unit 29 30 minutes before the present (for example, 19:00 to 19:30) (step S202). Further, the EC observation data extraction unit 40 extracts nd past EC observation data one by one from the EC observation data storage unit 32 in order (step S203). Next, the cumulative square error calculation unit 42 calculates a cumulative square error between the extracted past EC observation data and the estimated date EC observation data extracted in step S202 (step S204). In the present embodiment, accumulation is performed based on the estimated date EC observation data and the past date EC observation data in the past 30 minutes (for example, from 19:00 to 19:30) in the extracted estimated date EC observation data. A square error is calculated. Further, in the present embodiment, errors (total of 6 errors) between the estimated date EC observation data and the past EC observation data every 5 minutes in the 30 minutes are calculated, and the calculated errors are multiplied. The sum of the measured values is taken as the cumulative square error. Further, only the value of the accumulated square error before being stored in the storage areas (M1 to M10) in step S209 described later is referred to as a calculated value T. Further, as described above, the calculated value T has association data for associating with the past EC observation data used when calculating the calculated value T.

  Next, 1 is added to the calculation number I according to a command from the control unit 22, and the calculation number I after the addition is calculated (step S205). Next, the approximate determination unit 44 compares the calculated squared error value T acquired in step S204 with the values of the accumulated squared errors stored in the storage areas (M1 to M1) (step S206, S207, S208). Note that the comparison between the acquired calculated squared error value T and the accumulated squared error values stored in the storage areas (M1 to M10) is stored in each of the storage areas (M1 to M10). It is repeated up to 10 times until a calculated value T smaller than the accumulated square error value is found (steps S206, S207, S208). Specifically, if it is determined that the value of the cumulative square error stored in the storage area (M1 to M10) is smaller than the calculated value T (YES in step S206), the process proceeds to step S207 and is repeated. 1 is added to the value of the variable n (step S207). Then, the process proceeds to step S208, and if it is determined that the repetition variable n is less than 10, the process returns to step S206, and the cumulative square error stored in the other storage area (corresponding area among M1 to M10) is returned. The value and the calculated value T are compared.

  On the other hand, if it is determined in step S206 that the calculated value T is smaller than the value of the cumulative square error stored in the storage area (M1 to M10) (NO in step S206), the process proceeds to step S209. Subsequently, the approximation determination unit 44 replaces the value of the cumulative square error stored in the corresponding area among the storage areas M1 to M10 that are the comparison targets with the calculated value T (step S209). Then, it progresses to step S210, rewrites the value of the repetition variable n to 1, and returns to step S203. In the first 10 times (when the calculation count I is 1 to 10) in the replacement of the calculated value T in step S209, for example, when the calculation count I = 1, the value of the storage area M1 is replaced, and the calculation count I When = 2, the value of ∞ stored in all the storage areas (M1 to M10) is sequentially replaced with the calculated value T, for example, the value of the storage area M2 is replaced.

  If it is determined in step S208 that the repetition variable n is greater than 10 (YES in step S208), the process proceeds to step S211 to determine whether or not the cumulative square error has been calculated more than nd times (step S211). When it is determined YES in step S211, the estimated EC value calculation unit 46 calculates the calculated value T from the association data of each calculated value T stored in the storage area (M1 to M10). Acquire past EC observation data used for. Further, the estimated EC value calculation unit 46 sets time series data of EC values (hereinafter referred to as individual EC values) from the current time to the future one hour (for example, 19:30 to 20:30) in each past EC observation data. Are extracted and stored in the storage areas (M1 to M10) (step S212). On the other hand, if NO in step S211, the process proceeds to step S210, the repetitive variable n is rewritten to 1, and then the process returns to step S203. Through the processing from step S201 to step S211, time series data (hereinafter referred to as individual EC data) of the top 10 individual EC values having a small cumulative square error are stored in the storage area (M1 to M10). Will be.

  Next, after step S212, the estimated EC value calculation unit 46 calculates an average value of the top 10 individual EC data with a small cumulative square error stored in the storage area (M1 to M10), and calculates the EC value. Obtain (step S213). The time-series data (EC data) of the EC value is stored in a storage area other than M1 to M10 in the storage unit 30. Next, 10 pieces of individual EC data and EC data information are transmitted to the display unit 26 based on a command from the control unit 22, and the display unit 26 outputs the data to the screen in time series (step S214). ). Through the above processing, a time-series EC value for one hour in the future is calculated, and the time-series data is output to the display unit 26.

  Next, among the calculation processes of the rainwater inflow facility management support system 10 described above, the flow value calculation process in the flow rate estimation unit 36 will be described. The flow rate value calculation process in the flow rate estimation unit 36 is performed based on a command from the control unit 22.

  FIG. 4 is a flowchart showing a flow rate data calculation process for one hour after observation in the flow rate estimation unit 36. Note that the calculation process of the flow rate data for one hour after the observation is similar to the calculation process of the flow rate value at the time of observation, so the description of the similar part is simplified.

  As shown in FIG. 4, first, the sunny day EC observation data extraction unit 48 extracts the past EC observation data on the sunny day identified from the EC observation data storage unit 32 (step S301). Next, the day-by-day EC observation data extraction unit 50 further extracts Sunday past EC observation data from among a plurality of past EC observation data on a fine day extracted in step S301 (step S302). Further, the day-by-day EC observation data extraction unit 50 calculates clear-day EC observation data (step S303).

  Next, the dilution rate calculation unit 52 acquires time-series data of EC values for the future one hour calculated by the EC estimation unit 34 (step S304). Furthermore, time-series data of EC observation values corresponding to the time of the EC value acquired in step S304 is extracted from the clear sky day EC observation data calculated in step S303 (step S305).

  Further, the dilution rate calculation unit 52 performs the EC observation value (EC (wet)) on the sunny day extracted in step S305 and the EC observation value on the rainy day acquired in step S304. )), The dilution rate Z is calculated by the above-described equation (1) (step S306). The dilution rate Z is calculated in a time series in the time (1 hour) of the acquired EC value.

  Next, the estimated flow rate value calculation unit 54 creates the formula shown by the above-described formula (2) (step S307).

  Further, the estimated flow rate value calculation unit 54 calculates the flow rate value for one hour in the future in time series by substituting the value of the dilution rate Z calculated in step S306 into the formula created in step S307. Step S308). Through the processing as described above, a flow rate value for one hour in the future is calculated based on the EC value calculated by the EC estimation unit 34. As a result, it is possible to predict a flow rate change for one hour in the future.

  Next, among the calculation processing of the rainwater inflow facility management support system 10 described above, the water quality value calculation processing in the water quality estimation unit 36 is performed in the same procedure as described above. Then, water quality data for one hour in the future is calculated from the flow rate value calculated by the flow rate estimation unit 36, and a water quality change for one hour in the future can be predicted from the calculated water quality data.

  In the rainwater inflow facility management support system 10 configured as described above, the flow rate value can be acquired from the estimated date EC observation data and the EC data acquired based on the past EC observation data. Therefore, it is possible to estimate / predict the change in flow rate over a long period of time, stably and accurately without being restricted by the observation location. Further, the water quality value can be obtained based on the flow rate value. As described above, since the water quality value can be obtained based on the EC observation data, it is possible to estimate / predict the water quality change in a long term, stably and accurately without being restricted to the observation location. As a result, it is possible to perform appropriate operation management of rainwater inflow facilities such as a combined sewer.

  In the rainwater inflow facility management support method, the dilution rate is calculated by dividing the EC observation value of sewage on a clear day by the EC observation value of sewage on a rainy day, and the flow rate value is calculated based on the dilution rate. . Therefore, the flow rate can be estimated / predicted using the EC meter 12. As a result, the flow rate can be estimated / predicted over a long period of time, stably and accurately, without being restricted by the observation location, as compared with the case where a flow meter is used.

  Further, the EC estimation unit 34 calculates a cumulative square error between the estimated date EC observation data and the past EC observation data, extracts the top ten past EC observation data with a small cumulative square error, and averages them. The EC value is calculated from the value. For this reason, it is possible to extract past EC observation data that is closer to the actually observed data, and as a result, the accuracy of the calculated EC value is improved.

  Further, the flow rate estimation unit 36 calculates an average value of EC observation data of sewage on a sunny day on a plurality of Sundays corresponding to the day of the week of EC observation values. For this reason, it becomes possible to acquire more reliable sunny day EC observation data.

  Further, the flow rate estimation unit 36 creates a formula with the dilution rate Z as an input and the flow rate as an output. For this reason, it becomes possible to calculate the flow rate value according to the rainfall situation using this equation.

  Although one embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and can be implemented in various modifications.

  In the above-described embodiment, the EC observation data extraction unit 40 extracts all nd past EC observation data stored in the EC observation data storage unit 32, and the cumulative square error calculation unit 42 extracts nd cumulative square errors. However, the accumulated square error to be calculated is not limited to nd, but a past EC observation data having a number smaller than nd is extracted, and the accumulated square error of the extracted number is calculated. May be.

  Further, in the above-described embodiment, the EC estimation unit 34 estimates the flow rate at the time of observation using the EC value at the time of observation, acquires the estimated date EC observation data for the past 30 minutes, and acquires 1 hour in the future. EC value is calculated. However, the time zone of the estimated date EC observation data to be acquired and the time zone of the calculated EC value are not limited to 30 minutes and 1 hour, respectively. It is also possible to obtain the EC value for 2 hours in the future. Further, the rainwater inflow facility management support system 10 may be used to verify the past flow rate and water quality by calculating the past flow value and water quality value from the EC observation data observed in the past.

  Further, in the above-described embodiment, the EC estimation unit 34 obtains 10 pieces of past EC observation data having a small value of the error among cumulative square errors in order from the top, and averages the 10 past EC observation data. Although the value is EC data, the number of EC observation data acquired by the accumulated square error is not limited to 10 and may be another number such as 1 or 15, for example.

  In the above-described embodiment, the EC observation data extraction unit 40 extracts the EC observation data based on the day of the week when the EC value is observed by the EC meter 12 so as to extract the EC observation data on a sunny day on Sunday. However, the standard of the data extracted from the EC observation data on a clear day is not limited to the day of the week. For example, it may be based on whether it is a weekday or a holiday, a festival, etc. It may be based on the day when the special event is held. In addition, the next day when the typhoon passes may be used as a reference, or seasons such as spring, summer, autumn, and winter may be used as a reference, or other criteria may be set.

  In the above-described embodiment, the functions of the rainwater inflow facility management support system 10, the rainwater inflow facility management support device 20, and the like are specified as specific hardware and performed as function-specific means. The unit may be realized by software including a program, or at least a part thereof may be realized by hardware. For example, all or a part of the processing in the control unit 22 may be realized on a computer by one or a plurality of programs, and at least a part thereof may be realized by hardware.

  In the above-described embodiment, each time the cumulative square error is calculated in the calculation of EC data in the EC estimation unit 34, the calculated calculated value T and the cumulative square stored in the storage areas (M1 to M10). In comparison with the error value, first, the cumulative square error is calculated for all the nd past EC observation data stored in the EC observation data storage unit 32, and the calculated cumulative square error is calculated. You may make it memorize | store the EC observation data after one hour in the future in the storage area (M1-M10) with small value among the calculated values T.

  Examples of the present invention will be described below.

  First, the result of the EC value obtained by the EC estimation unit will be shown.

Table 1 shows the results of EC observations measured in the combined sewer. Tables 2 and 3 show an example of the result of the EC value calculated by the EC estimation unit. EC value observation (hereinafter referred to as EC observation) was performed using an EC meter on June 22, 2007, which is a rainy day. Further, the EC value prediction (hereinafter referred to as EC prediction) indicates that performed from 19:00 to 21:30 on June 22, 2007 (the portion surrounded by the wavy line A in Table 1). Specifically, based on EC observation values from 19:00 to 19:30, EC prediction from 19:30 to 20:30 is performed, and based on EC observation values from 20:00 to 20:30, EC prediction was performed from 20:30 to 21:30.

  As shown in Table 2, the graph of 10 EC values from 19:30 to 20:30 obtained based on EC observation values from 19:00 to 19:30 is from 19:30 to 20:30 It did not deviate significantly from the graph of EC observations. Moreover, the graph of the EC value from 19:30 to 20:30 obtained by averaging the graphs of the 10 EC values became similar to the graph of the EC observation value from 19:30 to 20:30.

  Moreover, as shown in Table 3, the graph of 10 EC values from 20:30 to 21:30 obtained based on EC observation values from 20:00 to 20:30 is 20:30 to 21:00 It did not deviate significantly from the graph of EC observations up to half. Also, the EC value graph from 20:30 to 21:30 approximated the EC observed value graph from 20:30 to 21:30.

  From the above results, it was found that the EC estimation unit can accurately perform EC prediction.

  Next, the result of the flow rate data obtained by the flow rate estimation unit will be described.

Table 4 shows a comparison between the time-series change of the dilution rate calculated by the flow rate estimation unit and the time-series change of the observed flow rate value (hereinafter referred to as the flow rate observation value). Table 5 shows a comparison between the graph of the flow rate estimation formula using the dilution rate as a variable and the flow rate observation value. Table 6 shows a comparison between a graph of the estimated flow rate value calculated by the flow rate estimation unit and a time-series change in the observed flow rate value. EC observation and flow observation were performed using an EC meter and a pump from 0:00 on June 10, 2007 to 16:00 on June 11, 2007, which are rainy days. Further, the prediction of the flow rate value (hereinafter referred to as the flow rate prediction) was performed from 0:30 on June 10, 2007 to 16:30 on June 11, 2007. The values of the coefficients α, β, and γ in the flow rate estimation formula used in the flow rate prediction were set to 0.1835, −0.5257, and 1.0337, respectively. The value of the correlation coefficient ^{R 2} in this case was 0.8268.

  As shown in Table 4, the EC observation value (EC (dry)) of sunny day sewage that was extracted from the EC observation data storage unit and calculated by the flow rate estimation unit was calculated on June 10, 2007, which is a rainy day. The graph of the dilution rate (EC (dry) / EC (wet)) divided by the EC observation value (EC (wet)) of sewage and the graph of the flow rate observation value (Q) were similar. In particular, it can be seen that the graph of the flow rate observation value (Q (+30)) obtained by shifting the time by 30 minutes and the graph of the dilution rate (EC (dry) / EC (wet)) are more similar. Moreover, it turned out from these results that the value of the flow rate can be predicted from the dilution rate (EC (dry) / EC (wet)).

Further, as shown in Table 5, the flow rate value calculated by the flow rate estimation equation (Q (+30)) shifted by 30 minutes in time and the flow rate observation value shifted by 30 minutes in time (Q ( the value of the correlation coefficient ^{R 2} between +30)) became 0.8268. From this result, it was found that the flow rate value (Q (+30)) calculated by the flow rate estimation equation and the flow rate observation value (Q (+30)) have no significant error. Therefore, it was found that the flow rate value can be predicted using the flow rate estimation formula with the dilution rate (EC (dry) / EC (wet)) as an input.

  Moreover, as shown in Table 6, it turned out that the graph of the flow rate value calculated by the flow rate estimation formula approximates the time series change of the flow rate observation value.

  From the above results, it was found that the flow rate can be accurately predicted based on the dilution rate.

  Next, it shows about the result of the water quality data obtained by the water quality estimation part.

Table 7 shows a comparison of time-series changes in the graph of the water quality value calculated by the water quality estimation unit and the observed water quality value (hereinafter referred to as the water quality observation value). Water quality observation and water quality change prediction (hereinafter referred to as water quality prediction) were performed from 12:00 on July 14, 2007 to 12:00 on July 14, 2007, which are rainy days. In addition, the load runoff coefficient C in the water quality estimation model formula used in water quality prediction is 0.00005, the sediment load P is 0.10, the flow coefficient m is 1.4, and the clear sky load DWL is 99.3. Set.

  As shown in Table 7, it was found that the graph of the water quality value calculated by the water quality estimation model formula approximates the time series change of the water quality observation value.

  From the above results, it was found that the water quality can be accurately predicted based on the flow rate. In addition, considering the above results, it was found that water quality prediction can be accurately performed based on the EC observation values observed with the EC meter.

  The rainwater inflow facility management support device, the rainwater inflow facility management support system, the rainwater inflow facility management support method, and the program that functions as the rainwater inflow facility management support device are used in the service industry that manages the operation of the rainwater inflow facility. can do.

It is a figure which shows the structure of the rainwater inflow facility management assistance system which concerns on one embodiment of this invention. It is a flowchart which shows the calculation process of the flow value at the time of observation in the flow volume estimation part in FIG. It is a flowchart which shows the calculation process of EC value for the time after observation in the EC estimation part in FIG. It is a flowchart which shows the calculation process of the flow volume value for the time after observation in the flow volume estimation part in FIG.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 ... Rainwater inflow facility management support system 12 ... EC meter 20 ... Rainwater inflow facility management support device 29 ... Observation value storage part 30 ... Storage part 32 ... EC observation data storage part (conductivity observation data storage part)
33 ... Rainy weather EC observation data storage part (rainy conductivity observation data storage part)
34 ... EC estimation unit 36 ... Flow rate estimation unit 38 ... Water quality estimation unit 40 ... EC observation data extraction unit (conductivity observation data extraction unit)
42 ... Cumulative square error calculation unit 44 ... Approximate discrimination unit 46 ... Estimated EC value calculation unit (conductivity value calculation unit)
48 ... Sunny day EC observation data extraction part (Sunny day conductivity observation data extraction part)
50 ... EC observation data extraction part by day of week (conductivity observation data extraction part by day of the week)
52 ... Dilution rate calculation unit 54 ... Estimated flow rate value calculation unit

Claims (15)

A rainwater inflow facility management support method for estimating the flow rate or water quality of the sewage from the conductivity of the sewage in the rainwater inflow facility,
Calculate the dilution rate by dividing the conductivity value of sewage on a clear day observed by a conductivity meter installed in the rainwater inflow facility by the conductivity value of sewage on a rainy day observed by the conductivity meter, A rainwater inflow facility management support method characterized by estimating a flow rate of sewage based on the dilution rate. Furthermore, the past conductivity observation data that is time series data of a plurality of conductivity values observed in the past in the rainwater inflow facility is stored in the conductivity observation data storage unit,
Storing the estimated daily conductivity observation data, which is time series data of the conductivity value of sewage observed on the estimated date by the conductivity meter, in the observation value storage unit;
Extracting the estimated daily conductivity observation data from the observation value storage unit, and sequentially extracting the past conductivity observation data from the conductivity observation data storage unit,
It is determined whether or not the past conductivity observation data extracted by the conductivity observation data extraction unit approximates the estimated day conductivity observation data, and a future sewage flow rate is predicted based on the dilution rate. The rainwater inflow facility management support method according to claim 1.   The rainwater inflow facility management support method according to claim 1 or 2, wherein the estimation / prediction of the flow rate is performed using a model formula that takes the dilution rate as an input and outputs the flow rate.   The rainwater inflow facility management support method according to claim 1, wherein water quality is estimated / predicted based on the estimated / predicted flow rate. A rainwater inflow facility management support device that estimates the flow rate or quality of the sewage from the conductivity of the sewage in the rainwater inflow facility,
A storage unit for storing the calculated data;
A conductivity observation data storage unit for storing past conductivity observation data that is time-series data of a plurality of conductivity values observed in the past in the rainwater inflow facility;
An observed value storage unit for storing an estimated daily conductivity observation value, which is a conductivity value of sewage observed on an estimated date by the conductivity meter;
Sunny day conductivity observation data extraction unit for extracting past conductivity observation data of sewage observed on a sunny day from the conductivity observation data storage unit,
A dilution rate calculation unit that calculates the dilution rate by dividing the value of the past conductivity observation data of the sewage on the clear day by the estimated date conductivity value of the sewage,
A rainwater inflow facility management support apparatus, comprising: an estimated flow value calculation unit that calculates a flow value that is a flow value of sewage to be estimated based on the dilution rate. Furthermore, a storage unit for storing calculated data;
A conductivity observation data storage unit that stores past conductivity observation data that is time-series data of a plurality of conductivity values observed in the past in the rainwater inflow facility;
An observed value storage unit for storing estimated daily conductivity observation data, which is time-series data of conductivity values of sewage observed by the conductivity meter on the estimated date;
Extracting the estimated daily conductivity observation data from the observation value storage unit, and a conductivity observation data extraction unit for sequentially extracting the past conductivity observation data from the conductivity observation data storage unit;
An approximate discriminator that discriminates whether or not the past conductivity observation data extracted in the conductivity observation data extraction unit approximates the estimated day conductivity observation data;
A conductivity value calculation unit for determining a conductivity value of the future sewage from the past conductivity observation data determined to be approximate in the approximation determination unit;
6. The rainwater inflow facility management support apparatus according to claim 5, wherein the estimated flow rate value calculation unit calculates the dilution rate from the future sewage conductivity value and calculates a flow rate value to be predicted. .   The rainwater inflow facility management according to claim 6, wherein the approximate discrimination unit discriminates whether or not the estimated daily conductivity observation data and the past conductivity observation data are approximated in a past common time series. Support device. And a cumulative square error calculation unit that calculates a cumulative square error between the estimated daily conductivity observation data and the past conductivity observation data extracted in the conductivity observation data extraction unit,
The rainwater inflow facility management support device according to claim 6 or 7, wherein the approximation determination by the approximation determination unit is performed based on the accumulated square error calculated by the accumulated square error calculation unit.   The approximation determination unit causes the storage unit to store data related to the past conductivity observation data of the top one or a plurality of tops although the cumulative square error calculated by the cumulative square error calculation unit is small. Item 9. The rainwater inflow facility management support device according to any one of Items 6 to 8.   The rainwater inflow facility management support device according to any one of claims 5 to 9, further comprising a day-by-day conductivity observation data extraction unit that extracts the past conductivity observation data on a clear day by day of the week. . The sunny day conductivity observation data extraction unit extracts a plurality of sunny day past conductivity observation data from the conductivity observation data storage unit,
The rainwater inflow facility management support device according to any one of claims 5 to 10, wherein an average value of the extracted values of the past conductivity observation data on the plurality of sunny days is calculated.   12. The calculation of the flow rate value in the estimated flow rate value calculation unit is performed using a model formula having the dilution rate as an input and a flow rate as an output. 12. Rainwater inflow facility management support device.   The rainwater inflow facility management support apparatus according to any one of claims 5 to 12, wherein water quality is estimated / predicted based on the flow rate value. The rainwater inflow facility management support device according to any one of claims 5 to 13,
A conductivity meter for observing the conductivity of sewage or sewage in a rainwater inflow facility;
A rainwater inflow facility management support system characterized by comprising:   A program for causing a computer to function as the rainwater inflow facility management support device according to any one of claims 5 to 13.

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