Information processing device, information processing method, and water treatment system

Abstract

The present invention comprises: an information acquisition unit that acquires increase / decrease information pertaining to an increase / decrease in people in at least one of a first region and a second region corresponding to the first region; and a water amount prediction unit that predicts a first water amount for treated water required in the first region on the basis of the acquired increase / decrease information.

Description

Information processing device, information processing method, and water treatment system

[0001] The present disclosure relates to an information processing device, an information processing method, and a water treatment system.

[0002] In a water purification plant, for example, a water treatment facility (water purification facility) is installed that performs various operations on raw water (hereinafter also referred to as water to be treated), such as river water, well water, etc. Specifically, such a water treatment facility performs various operations to produce purified water based on, for example, a predicted amount of purified water (hereinafter also referred to as treated water) that needs to be produced (see Patent Document 1).

[0003] Japanese Patent Application Laid-Open No. 2022-044738

[0004] In the water treatment facility described above, it is desirable to accurately predict, for example, the amount of purified water that needs to be produced.

[0005] The information processing device of the present disclosure has an information acquisition unit that acquires increase / decrease information regarding an increase / decrease in the number of people in at least one of a first area and a second area corresponding to the first area, and a water volume prediction unit that predicts a first water volume of treated water required in the first area based on the acquired increase / decrease information.

[0006] According to the information processing device, information processing method, and water treatment system of the present disclosure, it is possible to accurately predict the amount of purified water that needs to be produced.

[0007] FIG. 1 is a diagram illustrating the configuration of a water treatment system 1000 according to a first embodiment. FIG. 2 is a diagram illustrating the configuration of water treatment equipment 100 according to the first embodiment. FIG. 3 is a diagram illustrating the hardware configuration of a control device 1. FIG. 4 is a functional block diagram of the control device 1 according to the first embodiment. FIG. 5 is a flowchart illustrating the water demand prediction process according to the first embodiment. FIG. 6 is a diagram illustrating the water demand prediction process according to the first embodiment. FIG. 7 is a diagram illustrating the water demand prediction process according to the first embodiment. FIG. 8 is a diagram illustrating the water demand prediction process according to the first embodiment. FIG. 9 is a diagram illustrating the configuration of a water treatment system 2000 according to a second embodiment.

[0008] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. However, such descriptions should not be interpreted in a limiting sense, and do not limit the subject matter described in the claims. Furthermore, various changes, substitutions, and modifications can be made without departing from the spirit and scope of the present disclosure. Furthermore, different embodiments can be combined as appropriate.

[0009] [Water Treatment System 1000 in First Embodiment] First, a configuration example of water treatment system 1000 in the first embodiment will be described. Fig. 1 is a diagram illustrating the configuration of water treatment system 1000 in the first embodiment. Fig. 2 is a diagram illustrating the configuration of water treatment facility 100 in the first embodiment. Note that the positions and numbers of pumps and pipes in the following example are merely examples and are not limited to these.

[0010] The water treatment system 1000 is, for example, a water purification system installed in a water supply plant. Specifically, the water treatment system 1000 includes, for example, a control device 1 (hereinafter also referred to as an information processing device 1), a monitoring device 2, and a water treatment facility 100. In the example shown in Fig. 1 , the control device 1 and the monitoring device 2 can access each other via a network (not shown) such as Ethernet (registered trademark).

[0011] The water treatment facility 100 is, for example, a facility that produces treated water by performing water purification treatment on water to be treated.

[0012] Specifically, as shown in FIG. 2 , the water treatment facility 100 includes, for example, a gritification basin 10, a receiving well 20, a mixing basin 30, a flocculation basin 40 (hereinafter also referred to simply as the flocculation basin 40), a sedimentation basin 50, a filtration basin 60, a purified water basin 70, a distribution basin 80, a pump P1, a pump P2, a pump P3, and a storage tank T.

[0013] Pump P1 is, for example, a pump provided in a pipe connecting a river or the like to settling basin 10. Specifically, pump P1 supplies the water to be treated taken from a river or the like to settling basin 10 in an amount and at a time interval designated in advance by, for example, the manager of water treatment facility 100 (hereinafter simply referred to as the manager).

[0014] The settling basin 10 is a tank into which, for example, the treated water (treated water taken from a river, etc.) supplied by pump P1 first flows, and is a tank in which sedimentation and removal of sediment and the like contained in the treated water is performed.

[0015] The receiving well 20 is, for example, a tank that adjusts the amount of water to be treated supplied from the settling basin 10 and supplies it to the mixing basin 30.

[0016] The mixing basin 30 is, for example, a tank in which a flocculant is injected into the water to be treated supplied from the receiving well 20 .

[0017] The flocculation basin 40 is a tank that, for example, agitates the water to be treated supplied from the mixing basin 30, thereby flocculating suspended solids contained in the water to be treated supplied from the mixing basin 30 with a coagulant to form flocs.

[0018] The settling basin 50 is, for example, a tank in which flocs contained in the water to be treated supplied from the flocculation basin 40 are allowed to settle and are separated from the water to be treated.

[0019] The filtration basin 60 is a tank that filters the water to be treated supplied from the settling basin 50 by using a filter body (not shown) made of, for example, sand, gravel, or the like.

[0020] The purified water reservoir 70 is, for example, a tank that temporarily stores the treated water supplied from the filtration reservoir 60 (for example, the treated water after being disinfected by chlorine downstream of the filtration reservoir 60) and supplies it to the distribution reservoir 80.

[0021] The pump P2 is, for example, a pump provided in a pipe connecting the purified water reservoir 70 and the distributing reservoir 80. Specifically, the pump P2 supplies the untreated water (treated water) from the purified water reservoir 70 to the distributing reservoir 80 in an amount and at a time interval predetermined by, for example, an administrator.

[0022] The distributing reservoir 80 temporarily stores the water to be treated (treated water) supplied from the purified water reservoir 70, for example, and supplies it to homes and the like (not shown).

[0023] The storage tank T is, for example, a tank for storing a flocculant to be injected into the water to be treated.

[0024] The pump P3 is, for example, a pump provided in a pipe (not shown) that connects the storage tank T and the mixing basin 30. Specifically, the pump P3 supplies the coagulant to the mixing basin 30 at an injection rate that corresponds to an injection rate designated in advance by, for example, an administrator.

[0025] The water treatment facility 100 may further include a pump (not shown) other than the pumps P1, P2, and P3. The water treatment facility 100 may also supply a chemical other than a coagulant (e.g., caustic soda) to the water to be treated.

[0026] The control device 1 is, for example, an electronic device having an electronic circuit, and more specifically, is, for example, one or more physical machines having a CPU (Central Processing Unit) and a memory.

[0027] The control device 1 then performs a process (hereinafter also referred to as a water demand prediction process) to predict, for example, the amount of treated water that needs to be produced on the target day for prediction (hereinafter also simply referred to as the target prediction day). In other words, the control device 1 predicts, for example, the amount of treated water that needs to be supplied to homes, etc. on the target prediction day. The target prediction day may be, for example, the day on which the water demand prediction process is performed (hereinafter also simply referred to as the day on which the water demand prediction process is performed) or the day after the day on which the water demand prediction process is performed (hereinafter also simply referred to as the next day).

[0028] Specifically, for example, when the prediction target dates are the current day and tomorrow, the control device 1 predicts the amount of treated water that needs to be supplied to homes, etc. by using information such as the actual amount of treated water supplied to homes, etc. (e.g., past and current results), the weather in the area to which the treated water is supplied (hereinafter also referred to as the first area) (e.g., the weather on the current day, the next day, and the day after), the temperature in the first area (e.g., the temperature on the current day, the next day, and the day after), and the day of the week (e.g., the day of the week on the current day, the next day, and the day after).

[0029] The monitoring device 2 is, for example, an electronic device having an electronic circuit. Specifically, the monitoring device 2 is, for example, one or more physical machines or one or more virtual machines having a CPU and a memory.

[0030] The monitoring device 2 then controls each water treatment facility 100 based on, for example, the results of the water demand prediction process performed by the control device 1 .

[0031] Specifically, the monitoring device 2 controls the amount of treated water (amount per unit time) taken from a river, etc., by controlling the opening degree of a valve (not shown) provided on the pump P1, for example, based on the prediction results by the control device 1.

[0032] In addition, the monitoring device 2 controls the amount of treated water (amount per unit time) supplied to homes, etc. and the water level of the water distribution tank 80, for example, by controlling the opening degree of a valve (not shown) provided on the pump P2 based on the prediction results by the control device 1.

[0033] In addition, the monitoring device 2 controls the injection rate of coagulant into the treated water, for example, by controlling the opening of a valve (not shown) provided in the pump P3 based on the prediction results by the control device 1.

[0034] For example, if multiple pumps including pump P1 are installed in a pipe connecting a river or the like with a sedimentation basin 10, the monitoring device 2 may calculate the amount of treated water (amount per unit time) that needs to be discharged from the multiple pumps including pump P1 based on the prediction results by the control device 1, and control the amount of treated water taken from the river or the like by operating a number of pumps that are capable of discharging treated water corresponding to the calculated amount.

[0035] Furthermore, for example, if multiple pumps including pump P2 are installed in the piping connecting the purified water reservoir 70 and the distribution reservoir 80, the monitoring device 2 may calculate the amount of treated water (amount per unit time) that needs to be discharged from the multiple pumps including pump P2 based on the prediction results by the control device 1, and control the amount of treated water supplied to homes, etc. and the water level of the distribution reservoir 80 by operating a number of pumps that are capable of discharging treated water corresponding to the calculated amount.

[0036] Furthermore, for example, if multiple pumps including pump P3 are installed in the piping connecting the storage tank T and the mixing basin 30, the monitoring device 2 may calculate the amount of coagulant (amount per unit time) that needs to be discharged from the multiple pumps including pump P3 based on the prediction results by the control device 1, and control the injection rate of coagulant into the treated water by operating a number of pumps that are capable of discharging the coagulant corresponding to the calculated amount.

[0037] Here, in the water treatment system 1000 as described above, for example, it is required to more accurately predict the amount of treated water that needs to be supplied to homes or the like on the prediction target date.

[0038] Therefore, the control device 1 in this embodiment acquires information (hereinafter simply referred to as "increase / decrease information") about an increase or decrease in the number of people in at least one of a first area to which treated water produced in the water treatment facility 100 is supplied and a second area corresponding to the first area. The second area is, for example, an area through which a predetermined or higher proportion of people travelling from other areas to the first area pass. Specifically, the second area is, for example, an area in which the nearest train station to the first area is located.

[0039] The control device 1 then predicts the amount of treated water that needs to be supplied to homes and the like in the first area (hereinafter referred to as the required water supply amount or the first water amount) based on, for example, the acquired increase / decrease information. Thereafter, the control device 1 controls the water treatment facility 100 based on, for example, the predicted required water supply amount.

[0040] In other words, the control device 1 in this embodiment predicts the required water supply volume on the prediction date by taking into account, for example, the increase or decrease in the number of people in a first area, or the increase or decrease in the number of people in a second area that can be determined to have a high correlation with the increase or decrease in the number of people in the first area.

[0041] This makes it possible for the control device 1 in this embodiment to improve the accuracy of predicting the required water supply amount on the prediction target day, for example.

[0042] [Control Device 1 in First Embodiment] Next, the configuration of the control device 1 in the first embodiment will be described. Fig. 3 is a diagram illustrating the hardware configuration of the control device 1.

[0043] 3, the control device 1 is a computer device having, for example, a CPU 101 which is a processor, a memory 102, a communication device 103, and a storage medium 104. The respective components are connected to each other via, for example, a bus 105.

[0044] The storage medium 104 has, for example, a program storage area (not shown) that stores a program 110 for performing the water demand forecasting process. The storage medium 104 also has, for example, an information storage area 130 that stores information used when performing the water demand forecasting process. The storage medium 104 may be, for example, a hard disk drive (HDD) or a solid state drive (SSD).

[0045] The CPU 101 performs the water demand prediction process by executing, for example, a program 110 loaded from the storage medium 104 into the memory 102 .

[0046] The communication device 103 accesses, for example, via a network such as Ethernet (not shown), the monitoring device 2, an operation terminal (not shown) through which an operator inputs necessary information, and other information processing devices (not shown) that can provide various information used in water demand prediction processing.

[0047] The control device 1 may include, for example, a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC). The water demand prediction process may be executed by, for example, the FPGA or the ASIC.

[0048] In addition, although the following description will be given assuming that the water treatment system 1000 has one control device 1, the present invention is not limited to this. Specifically, the water treatment system 1000 may have, for example, multiple control devices 1. The water demand prediction process may be performed in a distributed manner among the multiple control devices 1, for example.

[0049] [Functions of the Control Device 1] Next, a description will be given of the functions of the control device 1. Fig. 4 is a block diagram of the functions of the control device 1 in the first embodiment.

[0050] As shown in Figure 4, the control device 1 realizes various functions including an information management unit 111, an information acquisition unit 112, a water volume prediction unit 113, a factor identification unit 114, and a control execution unit 115 by organically cooperating with hardware such as a CPU 101 and a memory 102 and programs.

[0051] Furthermore, as shown in FIG. 4, the control device 1 stores, for example, schedule information 131, time division information 132, increase / decrease information 141, required water supply amount 142, and learning model MD in the information storage area 130.

[0052] The information management unit 111 stores various information, including, for example, schedule information 131 and time division information 132 input by the administrator via the operation terminal, in the information storage area 130. The schedule information 131 is, for example, information indicating days of the week, holidays, etc. (information included in a calendar). The time division information 132 is, for example, information indicating patterns for each condition (hereinafter also referred to as time division patterns) used when dividing the predicted value of the required water supply amount 142 into time divisions (for example, hourly divisions). Specifically, the time division information 132 is, for example, information indicating time division patterns for each day of the week and each weather type.

[0053] The information acquisition unit 112 acquires, for example, number-of-people information indicating the number of people located in a first area at a first timing (hereinafter also referred to as first number-of-people information) and number-of-people information indicating the number of people located in the first area at a timing prior to the first timing (hereinafter also referred to as second number-of-people information) from another information processing device (not shown). The number-of-people information is, for example, information generated by aggregating location information of mobile devices (e.g., smartphones) owned by people located in the first area. The first timing is, for example, the most recent timing among the timings covered by each obtainable number-of-people information.

[0054] Then, the information acquisition unit 112 calculates (generates) increase / decrease information 141 based on, for example, the first number-of-people information and the second number-of-people information. Specifically, the information acquisition unit 112 calculates, for example, the difference between the number of people indicated by the first number-of-people information and the number of people indicated by the second number-of-people information, and acquires the calculated difference as increase / decrease information 141. Thereafter, the information acquisition unit 112 stores the acquired increase / decrease information 141 in the information storage area 130, for example.

[0055] The information acquisition unit 112 may acquire, for example, multiple pieces of second number of people information at one or more timings prior to the first timing from another information processing device. The information acquisition unit 112 may then calculate, for example, the increase / decrease information 141 calculated based on the first number of people information and the multiple pieces of second number of people information. Specifically, in this case, the information acquisition unit 112 may calculate, for each of the multiple pieces of second number of people information, the difference between the number of people indicated by the first number of people information and the number of people indicated by the second number of people information, and acquire each of the calculated differences as the increase / decrease information 141.

[0056] The increase / decrease information 141 may be calculated in another information processing device, for example, and the information acquisition unit 112 may acquire the increase / decrease information 141 from the other information processing device (not shown).

[0057] The water volume prediction unit 113 predicts a required water supply volume 142 on the prediction target date based on, for example, the increase / decrease information 141 acquired by the information acquisition unit 112 .

[0058] Specifically, the water volume prediction unit 113 determines whether the increase / decrease information 141 acquired by the information acquisition unit 112 satisfies a predetermined condition (hereinafter simply referred to as the predetermined condition), for example. The predetermined condition is that the value indicated by the increase / decrease information 141 (the number of people who increased or decreased in the first region) is equal to or greater than a threshold value.

[0059] If the information acquisition unit 112 determines that the acquired increase / decrease information 141 does not satisfy the predetermined condition, in other words, if the increase / decrease in the number of people in the first region is small, the water volume prediction unit 113 calculates a predicted value of the required water supply volume 142 in the first region on the target prediction date, for example, by using a predetermined mathematical formula (hereinafter simply referred to as a mathematical formula) based on the actual value of the required water supply volume 142 in the first region on the day before the target prediction date (e.g., the day before the target prediction date) and the weather in the first region on the day before the target prediction date (e.g., the day before the target prediction date). The information acquisition unit 112 then references, for example, the schedule information 131 stored in the information storage area 130 to identify the day of the week on which the target prediction date will occur. The water volume prediction unit 113 then references, for example, the time division information 132 stored in the information storage area 130 to identify a time division pattern corresponding to the day of the week on which the target prediction date will occur and the weather in the first region on the target prediction date. The water volume prediction unit 113 then performs time division on the predicted value of the required water supply volume 142 in the first region on the target prediction day, for example, by using the identified time division pattern. That is, the water volume prediction unit 113 calculates, for example, the predicted value of the required water supply volume 142 in the first region on the target prediction day for each predetermined time period. The predetermined time period may be, for example, one hour. Note that the weather, etc. in the first region on the target prediction day may be acquired by the information acquisition unit 112 from another information processing device, for example.

[0060] On the other hand, for example, if the information acquisition unit 112 determines that the increase / decrease information 141 it acquires satisfies a predetermined condition—in other words, if it determines that the increase / decrease in the number of people in the first region is large—the water volume prediction unit 113 acquires a predicted value for the required water supply volume 142 for the first region on the target prediction date for each predetermined time period, for example, by using the learning model MD stored in the information storage area 130. The learning model MD is a learning model that has learned multiple pieces of training data (not shown), each of which includes, for example, the increase / decrease information 141 corresponding to the first region at a past timing and the actual value for the required water supply volume 142 for the first region at a past timing for each predetermined time period (hereinafter also referred to as the first actual value). Specifically, for example, the water volume prediction unit 113 inputs the increase / decrease information 141 acquired by the information acquisition unit 112 into the learning model MD, and acquires each value output from the learning model MD as a predicted value for the required water supply volume 142 for the first region on the target prediction date for each predetermined time period.

[0061] The training data used to train the learning model MD may include, for example, actual values ​​(hereinafter also referred to as actual increase values) of the increase in the required water supply volume 142 in the first region at a predetermined time point in the past, instead of the first actual value. Specifically, the actual increase value is, for example, the increase in the required water supply volume 142 relative to the first actual value when the number of people indicated by the increase / decrease information 141 is a predetermined number of people (e.g., the average or median of the number of people indicated by the increase / decrease information 141 acquired over a predetermined period in the past). That is, the learning model MD may be a learning model that outputs, for example, a predicted value (hereinafter also referred to as predicted increase value) of the increase in the required water supply volume 142 in the first region on the target prediction date for each predetermined time point. In this case, the water volume prediction unit 113 may calculate, for example, a predicted value (hereinafter also referred to as predicted reference volume value) of the required water supply volume 142 in the first region for the target prediction date for each predetermined time point by using a formula, and may also calculate the predicted increase value by using the learning model MD. Thereafter, the water volume prediction unit 113 may, for example, obtain the sum of the calculated reference volume prediction value and the increase volume prediction value as a predicted value for each specified hour of the required water supply volume 142 for the first region on the prediction target day.

[0062] Furthermore, the teacher data used to train the learning model MD may include, for example, parameter values ​​included in a formula instead of the first actual value. That is, the learning model MD may be, for example, a learning model that outputs parameter values ​​corresponding to the increase / decrease information 141. In this case, the water volume prediction unit 113 may calculate the first predicted value by using, for example, a formula in which the parameter values ​​output from the learning model MD are set.

[0063] Furthermore, the training data used to train the learning model MD may further include, for example, weather in the first region at a past time, the day of the week at a past time, etc. In this case, the water volume prediction unit 113 may further input, for example, the weather in the first region on the target prediction day, the day of the week on the target prediction day, etc., in addition to the increase / decrease information 141 acquired by the information acquisition unit 112.

[0064] That is, when the increase or decrease in the number of people in the first region is small, it can be determined that the prediction target day is a day (hereinafter also referred to as a normal day) on which it is possible to predict the required water supply volume 142 based on a pattern based on the day of the week, weather, etc. Therefore, in this case, the control device 1 predicts the required water supply volume 142 by using, for example, a formula prepared in advance or a time division pattern that is patterned in advance.

[0065] On the other hand, when there is a large increase or decrease in the number of people in the first region, for example, when an event is being held in the first region or when people are returning home to the first region in large numbers, it can be determined that the prediction target day is a day (hereinafter also referred to as a special day) on which it is difficult to predict the required water supply volume 142 based on patterns based on the day of the week, weather, etc. Therefore, in this case, the control device 1 predicts the required water supply volume 142 by using, for example, the learning model MD.

[0066] The factor identification unit 114 identifies the cause of the increase or decrease in the number of people in the first region (hereinafter also referred to as the first cause) based on, for example, the increase or decrease information 141 acquired by the information acquisition unit 112 .

[0067] Specifically, the factor identifying unit 114 identifies, for example, the timing of occurrence of an increase or decrease in the number of people (hereinafter simply referred to as occurrence timing) and the pace of increase or decrease in the number of people (hereinafter also referred to as increase or decrease pace) indicated by the increase or decrease information 141 acquired by the information acquiring unit 112. Then, the factor identifying unit 114 identifies, for example, a first occurrence factor corresponding to the combination of the identified occurrence timing and increase or decrease pace.

[0068] More specifically, for example, if the increase / decrease information 141 indicates that the number of people located in the first area has increased sharply since the previous day, the factor identification unit 114 determines that the period of the increase in people in the first area is approximately several days, and determines that the cause of the increase in people in the first area is the holding of an event in the first area. On the other hand, for example, if the increase / decrease information 141 indicates that the number of people located in the first area has increased gradually since the previous week, the factor identification unit 114 determines that the period of the increase in people in the first area is approximately several weeks, and determines that the cause of the increase in people in the first area is the concentration of people returning home to the first area.

[0069] The control execution unit 115 controls the water treatment facility 100 based on, for example, the predicted value of the required water supply volume 142 predicted by the water volume prediction unit 113. Specifically, the control execution unit 115 identifies control that needs to be performed in the water treatment facility 100 (e.g., control of the opening degrees of pumps P1, P2, and P3, etc.) based on, for example, the predicted value of the required water supply volume 142 predicted by the water volume prediction unit 113, and instructs the monitoring device 2 to perform the identified control. Furthermore, the control execution unit 115 identifies control that needs to be performed in the water treatment facility 100 based on, for example, the predicted value of the required water supply volume 142 predicted by the water volume prediction unit 113, and notifies the operator (via an operation terminal viewable by the operator) of the content of the identified control.

[0070] In addition, the control execution unit 115 may control the water treatment equipment 100 by using, for example, the predicted value of the required water supply volume 142 predicted by the water volume prediction unit 113, as well as the first occurrence factor identified by the factor identification unit 114.

[0071] [Water Demand Forecasting Process in the First Embodiment] Next, the water demand forecasting process in the first embodiment will be described. Fig. 5 is a flowchart illustrating the water demand forecasting process in the first embodiment. Figs. 6 to 8 are diagrams illustrating the water demand forecasting process in the first embodiment.

[0072] The information acquisition unit 112 acquires, for example, increase / decrease information 141 about an increase / decrease in the number of people in the first region (step S1 in FIG. 5).

[0073] Specifically, the information acquisition unit 112 acquires, for example, the increase / decrease information 141 calculated based on the first number-of-people information and the second number-of-people information. A specific example of the increase / decrease information 141 will be described below.

[0074] [Specific Example of Increase / Decrease Information 141] FIG. 6 is a diagram illustrating a specific example of the increase / decrease information 141. In FIG.

[0075] The increase / decrease information 141 shown in Figure 6 is set to, for example, the difference in number of people between the number of people indicated by the number of people information (first number of people information) in the first area today and the number of people indicated by the number of people information (second number of people information) in the first area a few days ago.

[0076] Specifically, in the increase / decrease information 141 shown in FIG. 6, for example, "3000 (people)" is set for "7 days ago," which sets the difference in number of people between the number indicated by the number of people information today and the number indicated by the number of people information 7 days ago; "1000 (people)" is set for "2 days ago," which sets the difference in number of people between the number indicated by the number of people information today and the number indicated by the number of people information 2 days ago; and "0 (people)" is set for "1 day ago," which sets the difference in number of people between the number indicated by the number of people information today and the number indicated by the number of people information 1 day ago.

[0077] That is, the increase / decrease information 141 shown in Figure 6 indicates, for example, that the number of people located in the first region today is 3,000 (people) higher than the number seven days ago. The increase / decrease information 141 shown in Figure 6 also indicates, for example, that the number of people located in the first region today is 1,000 (people) higher than the number two days ago. The increase / decrease information 141 shown in Figure 6 also indicates, for example, that the number of people located in the first region today has not increased from the number one day ago. A description of the other information included in Figure 6 will be omitted.

[0078] Returning to FIG. 5, the water volume prediction unit 113 determines whether or not the increase / decrease information 141 acquired in step S1 satisfies a predetermined condition (step S2 in FIG. 5), for example.

[0079] 6 indicates that the number of people located in the first region today is 1,000 more than the number of people two days ago. Therefore, if the predetermined condition is that the number of people is 500 or more more than the number of people two days ago, the water volume prediction unit 113 determines that the increase / decrease information 141 acquired in step S1 satisfies the predetermined condition.

[0080] As a result, for example, if it is determined that the increase / decrease information 141 acquired in step S1 satisfies the predetermined condition (YES in step S2 in FIG. 5), the water volume prediction unit 113 uses the learning model MD stored in the information storage area 130 to obtain a predicted value for each predetermined time of the required water supply volume 142 for the first region on the prediction target day (step S3 in FIG. 5). Specific examples of predicted values ​​for each predetermined time of the required water supply volume 142 will be described below.

[0081] 7 is a diagram illustrating a specific example of a predicted value of the required water supply amount 142 for each predetermined time period. The following describes a case where the predetermined time period is one hour.

[0082] The predicted value of the required water supply amount 142 shown in FIG. 7 is set to, for example, a predicted value for each hour of the required water supply amount 142 on the target prediction day.

[0083] Specifically, the predicted value of the required water supply amount 142 shown in FIG. 7 is, for example, a value predicted one hour after the timing when the water demand prediction process is executed. ３ / h) is set, and the predicted value two hours after the water demand prediction process is executed is set to "2 hours later." ３7, for example, a predicted value 47 hours after the timing when the water demand prediction process is executed is set to "550 (m ３ / h) is set, and the predicted value 48 hours after the timing of the water demand prediction process is set to "48 hours later." ３ / h) is set. Explanation of other information included in FIG. 7 will be omitted.

[0084] The learning model MD used in step S3 of Figure 5 may be generated by learning training data DT that includes, for example, each item included in the increase / decrease information 141 shown in Figure 6 and each item included in the required water supply volume 142 shown in Figure 7, as shown in Figure 8.

[0085] Returning to Figure 5, in this case, the factor identification unit 114 identifies the first occurrence factor for the increase or decrease in the number of people in the first area, for example, based on the increase or decrease information 141 acquired in step S1 (step S4 in Figure 5).

[0086] On the other hand, for example, if it is determined that the increase / decrease information 141 acquired in step S1 does not satisfy the predetermined condition (NO in step S2 in FIG. 5), the water volume prediction unit 113 and the factor identification unit 114 do not perform steps S3 and S4. In this case, the water volume prediction unit 113 refers to, for example, the schedule information 131 and the time division information 132 stored in the information storage area 130, and predicts the required water supply volume by using a prepared mathematical formula or a pre-defined time division pattern.

[0087] In this way, the control device 1 in this embodiment acquires, for example, increase / decrease information 141 about an increase / decrease in the number of people in at least one of the first area and the second area, and predicts a required water supply volume 142 in the first area based on the acquired increase / decrease information 141. Then, the control device 1 controls, for example, the water treatment facility 100 that produces treated water from water to be treated based on the required water supply volume 142 in the first area.

[0088] Specifically, the control device 1 acquires, for example, first number-of-people information indicating the number of people located in at least one of a first region and a second region at a first timing. The control device 1 also acquires, for example, second number-of-people information indicating the number of people located in either the first region or the second region at each timing, for example, at one or more timings prior to the first timing. The control device 1 then calculates the increase / decrease information 141 based on, for example, the acquired first number-of-people information and the second number-of-people information corresponding to each of the acquired one or more timings.

[0089] Furthermore, for example, when the acquired increase / decrease information 141 satisfies a predetermined condition, the control device 1 inputs the increase / decrease information 141 to the learning model MD that has learned the teacher data DT including the population increase / decrease information 141 in the first area and the required water supply amount 142 in the first area, and acquires the water amount corresponding to the value output from the learning model MD as the required water supply amount 142. Then, the control device 1 controls the water treatment facility 100 based on the acquired required water supply amount 142, for example.

[0090] Furthermore, for example, when the acquired increase / decrease information 141 satisfies a predetermined condition, the control device 1 identifies the first occurrence factor corresponding to the increase / decrease information 141. Then, the control device 1 controls the water treatment facility 100 based on, for example, the required water supply amount 142 and the first occurrence factor.

[0091] That is, the control device 1 in this embodiment determines whether the prediction target day is a normal day or an anomalous day by, for example, referring to the increase / decrease information 141.

[0092] This allows the control device 1 to accurately determine whether the target prediction day is a normal day or an unusual day, for example. Therefore, the control device 1 can prevent the required water supply volume 142 from being predicted as if the target prediction day were a normal day, even though the target prediction day is an unusual day, and can accurately predict the required water supply volume for the target prediction day.

[0093] Therefore, in water treatment system 1000 of the present embodiment, it is possible to suppress, for example, the occurrence of an excess or deficiency of treated water produced in water treatment facility 100. Specifically, in water treatment system 1000, it is possible to suppress, for example, the occurrence of a situation in which treated water produced in water treatment facility 100 is in excess. Therefore, in water treatment system 1000, it is possible to reduce, for example, the costs associated with the production of treated water in water treatment facility 100 (fees for the use of chemicals such as coagulants and electricity).

[0094] Furthermore, in the water treatment system 1000, for example, by suppressing the occurrence of an excess or deficiency of treated water produced in the water treatment facility 100, it is possible to reduce the frequency of performing various operations in the water treatment facility 100 (for example, opening and closing valves provided in the pump P1, etc.). Therefore, in the water treatment system 1000, it is possible to reduce the workload of the operator of the water treatment facility 100, for example.

[0095] In addition, in step S11 of Figure 5, the information acquisition unit 112 may acquire, for example, increase / decrease information 141 regarding an increase / decrease in the number of people in a second region corresponding to the first region, together with or instead of the increase / decrease information 141 corresponding to the first region.

[0096] In other words, the increase / decrease information 141 in an area (second area) where people moving from other areas to the first area are likely to pass through can be determined to be information that is highly correlated with the increase / decrease in the number of people in the first area, and that occurs earlier in the timing of the increase / decrease in the number of people than the increase / decrease information 141 corresponding to the first area.

[0097] Therefore, the information acquisition unit 112 may acquire, for example, the increase / decrease information 141 corresponding to the second region in step S11 of Fig. 5. Then, the water volume prediction unit 113 may predict the required water supply volume on the prediction target day by using, for example, the increase / decrease information 141 corresponding to the second region in step S13 of Fig. 5.

[0098] This enables the water treatment system 1000 to, for example, predict the required water supply amount on the target prediction date at an earlier timing.

[0099] [Water Treatment System 2000 According to Second Embodiment] Next, a configuration example of a water treatment system 2000 according to a second embodiment will be described. Fig. 9 is a diagram illustrating the configuration of the water treatment system 2000 according to the second embodiment.

[0100] The water treatment system 2000 in this embodiment further includes, for example, a water treatment facility 200 different from the water treatment facility 100 described with reference to FIG. 1 and the like, and a pump P11.

[0101] The water treatment facility 200 is, for example, a water treatment facility that produces treated water to be supplied to homes or the like in an area different from the first area, and has a configuration similar to that of the water treatment facility 100. The distributing reservoir 180 in the water treatment facility 200 is connected to the distributing reservoir 80 in the water treatment facility 100 via a pipe L1, for example.

[0102] The pump P11 is, for example, a pump provided in the pipe L1. Specifically, the pump P11 supplies, for example, treated water stored in the distributing reservoir 180 to the distributing reservoir 80. The pump P11 also supplies, for example, treated water stored in the distributing reservoir 80 to the distributing reservoir 180. Note that the pump that supplies treated water stored in the distributing reservoir 80 to the distributing reservoir 180 may be, for example, a pump different from the pump P11.

[0103] Then, for example, when the water treatment facility 100 generates treated water based on the predicted value of the required water supply volume 142 and there is a surplus of treated water stored in the distributing reservoir 80, the control device 1 in this embodiment controls the pump P11 so that at least a portion of the treated water stored in the distributing reservoir 80 is supplied to the distributing reservoir 180 via the pipe L1. Also, for example, when the water treatment facility 100 generates treated water based on the predicted value of the required water supply volume 142 and there is a shortage of treated water stored in the distributing reservoir 80, the control device 1 controls the pump P11 so that at least a portion of the treated water stored in the distributing reservoir 180 is supplied to the distributing reservoir 80 via the pipe L1.

[0104] In other words, if a discrepancy occurs between the predicted value of the required water supply volume 142 calculated by the water demand prediction process and the actual value of the required water supply volume 142, there is a possibility that the water treatment equipment 100 (distribution reservoir 80) will experience, for example, an excess or shortage of treated water.

[0105] Therefore, the control device 1 in this embodiment controls the supply of treated water between the water treatment facilities 100 and 200, for example.

[0106] Specifically, for example, when the water treatment facility 100 produces an amount of treated water corresponding to the predicted value of the required supply water volume 142, the control device 1 determines whether the actual value of the required supply water volume 142 is less than the predicted value of the required supply water volume 142. As a result, if it is determined that the actual value of the required supply water volume 142 is less than the predicted value of the required supply water volume 142, the control device 1 performs control to supply treated water produced in the water treatment facility 200 from the water treatment facility 200 to the water treatment facility 100. Specifically, for example, when it is determined that the actual value of the required supply water volume 142 is less than the predicted value of the required supply water volume 142 by a predetermined value or more, the control device 1 performs control to supply treated water produced in the water treatment facility 200 from the water treatment facility 200 to the water treatment facility 100.

[0107] Furthermore, for example, when the water treatment facility 100 produces an amount of treated water corresponding to the predicted value of the required supply water volume 142, the control device 1 determines whether the actual value of the required supply water volume 142 is greater than the predicted value of the required supply water volume 142. As a result, when it is determined that the actual value of the required supply water volume 142 is greater than the predicted value of the required supply water volume 142, the control device 1 performs control to supply the treated water produced in the water treatment facility 100 from the water treatment facility 100 to the water treatment facility 200. Specifically, for example, when it is determined that the actual value of the required supply water volume 142 is greater than the predicted value of the required supply water volume 142 by a predetermined value or more, the control device 1 performs control to supply the treated water produced in the water treatment facility 100 from the water treatment facility 100 to the water treatment facility 200.

[0108] As a result, even if there is an excess or shortage of treated water in the water treatment facility 100, the control device 1 in this embodiment can eliminate the excess or shortage by controlling the supply of treated water between the water treatment facility 100 and the water treatment facility 200. Therefore, the water treatment system 2000 in this embodiment can further reduce the costs (fees for the use of chemicals such as coagulants and electricity) associated with the production of treated water in the water treatment facility 100, for example.

[0109] The control device 1 may, for example, supply treated water produced in the water treatment facility 100 to a plurality of water treatment facilities. The control device 1 may, for example, supply treated water produced in a plurality of water treatment facilities to the water treatment facility 100.

[0110] 1: Control device 2: Monitoring device 10: Settling basin 20: Receiving well 30: Mixing basin 40: Flocculation basin 50: Sedimentation basin 60: Filtration basin 70: Purified water reservoir 80: Distributing reservoir 100: Water treatment facility 101: CPU 102: Memory 103: Communication device 104: Storage medium 105: Bus 110: Program 111: Information management unit 112: Information acquisition unit 113: Water volume prediction unit 114: Cause identification unit 115: Control execution unit 130: Information storage area 131: Schedule information 132: Time division information 141: Increase / decrease information 142: Required water supply volume 180: Distributing reservoir 200: Water treatment facility 1000: Water treatment system 2000: Water treatment system DT: Teacher data L1: Piping MD: Learning model P1: Pump P2: Pump P3: Pump P11: Pump

Claims

1. An information processing device having an information acquisition unit that acquires information on increases and decreases in the number of people in at least one of a first area and a second area corresponding to the first area, and a water volume prediction unit that predicts a first volume of treated water required in the first area based on the acquired increase and decrease information.

2. The information processing device according to claim 1, further comprising a control execution unit that controls a water treatment facility that produces the treated water from the water to be treated based on the first water volume.

3. The information processing device described in claim 1, wherein the information acquisition unit acquires first number of people information indicating the number of people located in at least one of the first area and the second area at a first timing, acquires second number of people information indicating the number of people located in either the first area or the second area at each timing for one or more timings before the first timing, and generates the increase / decrease information based on the acquired first number of people information and the second number of people information corresponding to each of the acquired one or more timings.

4. The information processing device described in claim 2, wherein, when the acquired increase / decrease information satisfies a predetermined condition, the water volume prediction unit inputs the increase / decrease information into a learning model that has learned training data including information on the increase / decrease in the number of people in the first area and the amount of treated water required in the first area, and acquires the water volume corresponding to the value output from the learning model as the first water volume, and the control execution unit controls the water treatment equipment based on the acquired first water volume.

5. The information processing device according to claim 2, further comprising a factor identification unit that identifies a first occurrence factor corresponding to the increase / decrease information when the acquired increase / decrease information satisfies a predetermined condition, and the control execution unit controls the water treatment equipment based on the first water volume and the first occurrence factor.

6. An information processing method that acquires information on increases and decreases in the number of people in at least one of a first area and a second area corresponding to the first area, and predicts a first amount of treated water required in the first area based on the acquired information on increases and decreases.

7. A water treatment system having water treatment equipment and an information processing device that controls the water treatment equipment, wherein the information processing device has: an information acquisition unit that acquires increase / decrease information regarding an increase / decrease in the number of people in at least one of a first area and a second area corresponding to the first area; and a water volume prediction unit that predicts a first volume of treated water required in the first area based on the acquired increase / decrease information.

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