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

Abstract

To provide an information processing device, a water treatment system, an information processing method, and an information processing program that enable overall optimization of operation management.SOLUTION: An information processing device includes data acquisition unit that acquires operation data corresponding to each of a plurality of plants from the plurality of plants, a feature calculation unit that calculates feature information indicating features regarding the interrelation of each of the acquired operation data, and an information output unit that outputs each of the calculated feature information.SELECTED DRAWING: Figure 11

Description

[Technical field]

[0001] The present invention relates to an information processing device, a water treatment system, an information processing method, and an information processing program. [Background technology]

[0002] In a water treatment system (hereinafter simply referred to as a water treatment system) that includes multiple water treatment plants that treat the water to be treated, operation is managed so that the water quality of the treated water (hereinafter also referred to as effluent water) discharged from each water treatment plant into a river or the like meets a standard (hereinafter also referred to as a water quality standard) (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2023-001196 A Summary of the Invention [Problem to be solved by the invention]

[0004] Here, if there is a water treatment plant whose effluent water quality greatly exceeds the standard, it is possible to determine that the water treatment system has not achieved overall optimization of operation management, including costs, etc. (hereinafter, simply referred to as overall optimization) Therefore, in the water treatment system, for example, a method is required that enables overall optimization of operation management between each water treatment plant. [Means for solving the problem]

[0005] In order to achieve overall optimization of operation management as described above, the information processing device of the present invention has a data acquisition unit that acquires operation data corresponding to each of a plurality of plants from the plurality of plants, a feature calculation unit that calculates feature information indicating features of the interrelationships between the acquired operation data, and an information output unit that outputs the calculated feature information. Effect of the Invention

[0006] According to the information processing device, water treatment system, information processing method, and information processing program of the present invention, it becomes possible to achieve overall optimization of operation management. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a diagram illustrating an example of a configuration of a water treatment system 100 according to the first embodiment. [Diagram 2] FIG. 2 is a diagram illustrating a hardware configuration of information processing device 10 in the first embodiment. [Diagram 3] FIG. 3 is a block diagram of information processing device 10 according to the first embodiment. [Figure 4] FIG. 4 is a diagram illustrating information stored in the information storage area 130 in the first embodiment. [Diagram 5] FIG. 5 is a flowchart illustrating the information output process in the first embodiment. [Figure 6] FIG. 6 is a diagram for explaining a specific example of the operating data DT0 acquired from the water treatment plant 20. As shown in FIG. [Figure 7] FIG. 7 is a diagram for explaining a specific example of the plant information 131. As shown in FIG. [Figure 8] FIG. 8 is a diagram for explaining a specific example of the driving data DT0 stored in the information storage area 130. As shown in FIG. [Figure 9] FIG. 9 is a diagram for explaining a specific example of the driving data DT1. [Figure 10] FIG. 10 is a diagram for explaining a specific example of the distribution of the driving data DT1. [Figure 11] FIG. 11 is a diagram for explaining a specific example of the distribution of the driving data DT1. [Figure 12] FIG. 12 is a diagram illustrating a specific example of the facility information 132. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[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. In addition, various changes, substitutions, and modifications can be made without departing from the spirit and scope of the present disclosure. In addition, different embodiments can be appropriately combined.

[0009] [Water treatment system 100 according to the first embodiment] First, a water treatment system 100 in the first embodiment will be described. Fig. 1 is a diagram illustrating a configuration example of the water treatment system 100 in the first embodiment.

[0010] 1, the water treatment system 100 includes, for example, an information processing device 10, and a water treatment plant 20a, a water treatment plant 20b, and a water treatment plant 20c. The information processing device 10 communicates with each of the water treatment plants 20a, 20b, and 20c via a network NW such as the Internet.

[0011] In the following, the water treatment plants 20a, 20b, and 20c are collectively referred to simply as water treatment plants 20 or plants 20. In the following, the water treatment system 100 will be described as having three water treatment plants 20, but the water treatment system 100 may have, for example, two water treatment plants 20 or four or more water treatment plants 20.

[0012] The water treatment plant 20 is, for example, a sewage treatment plant that treats sewage. Specifically, the water treatment plant 20 has, for example, a primary sedimentation tank (not shown) that settles and separates suspended solids such as sand and soil contained in the water to be treated, a reaction tank (not shown) that treats the water to be treated supplied from the primary sedimentation tank by biological treatment such as the standard activated sludge method, and a final sedimentation tank (not shown) that settles and separates the activated sludge contained in the water to be treated discharged from the reaction tank and discharges it. In the water treatment plant 20, for example, the water to be treated discharged from the final sedimentation tank is sterilized in a sterilization treatment device (not shown), and the sterilized treated water is discharged into a river or the like. Hereinafter, the primary sedimentation tank, the tank, and the final sedimentation tank are collectively referred to simply as facilities.

[0013] Further, for example, a BOD sensor for measuring the BOD (Biochemical Oxygen Demand) concentration of the water to be treated, a TN sensor for measuring the TN (Total Nitrogen) concentration of the water to be treated, and a TP sensor for measuring the TP (Total Phosphorus) concentration of the water to be treated are attached to the water treatment plant 20. Then, each of the BOD sensor, the TN sensor, and the TP sensor measures the BOD concentration of the water to be treated, the TN concentration of the water to be treated, and the TP concentration of the water to be treated, respectively, at a predetermined timing (hereinafter simply referred to as the predetermined timing), such as at one-minute intervals, and transmits the measured values ​​to the information processing device 10.

[0014] The water treatment plant 20 is also provided with, for example, an addition device (not shown) that adds a chemical to the water to be treated. The addition device measures the amount of chemical added to the water to be treated at a predetermined timing (for example, the amount of chemical added between the previous predetermined timing and the current predetermined timing) and transmits the amount to the information processing device 10.

[0015] The water treatment plant 20 is also provided with, for example, a power monitoring device (not shown) that monitors the amount of power consumed in the water treatment plant 20. The power monitoring device measures the amount of power consumed in the water treatment plant 20 at a predetermined timing (for example, the amount of power consumed from the previous predetermined timing to the current predetermined timing) and transmits it to the information processing device 10.

[0016] In the following, the BOD sensor, TN sensor, TP sensor, dosing device, power monitoring device, etc. will be collectively referred to as simply "sensors." In the following, the BOD concentration, TN concentration, TP concentration, chemical amount, and power amount will be collectively referred to as simply "operation data."

[0017] The information processing device 10 is, for example, a physical machine or a virtual machine, and performs a process (hereinafter also referred to as information output process) of generating and outputting information (hereinafter also referred to as characteristic information) indicating the characteristics of the operating data acquired from each water treatment plant 20.

[0018] Specifically, the information processing device 10, for example, acquires operation data from a plurality of water treatment plants 20. Then, the information processing device 10, for example, calculates characteristic information indicating characteristics regarding the interrelationships between the acquired operation data. After that, the information processing device 10, for example, outputs the calculated characteristic information.

[0019] That is, the information processing device 10 in this embodiment outputs, for example, characteristics of the interrelationships of each operation data transmitted from each of a plurality of water treatment plants 20 including water treatment plants 20 owned by different owners, in a state in which a manager (hereinafter also simply referred to as a manager) who manages the plurality of water treatment plants 20 in an integrated manner and a driver (hereinafter also simply referred to as a driver) who operates each water treatment plant 20 can grasp the characteristics in an integrated manner. The water treatment plants 20 owned by different owners may be, for example, water treatment plants 20 owned by a different municipality from the other water treatment plants 20. In other words, the information processing device 10 outputs, for example, characteristics of the operation data transmitted from a wide variety of water treatment plants 20 having different operation management methods, treatment capacities of the water to be treated, installation areas, etc., in a state in which a manager or a driver (hereinafter collectively referred to as a manager, etc.) can grasp the characteristics in an integrated manner.

[0020] Thereby, the information processing device 10 in the present embodiment can output, for example, information (characteristic information) that serves as a basis for judging the degree of achievement of overall optimization in the multiple water treatment plants 20. Overall optimization is, for example, overall optimization of operation management in each of the multiple water treatment plants 20.

[0021] Therefore, for example, by referring to the information output from the information processing device 10, the manager can determine the degree of achievement of overall optimization in the multiple water treatment plants 20 that he manages, and can identify the water treatment plants 20 (hereinafter also referred to as specific water treatment plants 20) whose operation management methods need to be improved from the perspective of overall optimization. Therefore, for example, when the manager identifies a specific water treatment plant 20 based on the information output from the information processing device 10, the manager can improve the operation management methods of the specific water treatment plant 20 by comparing it with other water treatment plants 20, and can advance overall optimization in the multiple water treatment plants 20 that he manages.

[0022] Furthermore, the operator can determine whether or not it is necessary to improve the operation management method of the water treatment plant 20 that he or she operates from the perspective of overall optimization, for example, by referring to the information output from the information processing device 10. Therefore, for example, if the manager determines that it is necessary to improve the operation management method of the water treatment plant 20 that he or she operates as a result of referring to the information output from the information processing device 10, he or she can improve the operation management method of the water treatment plant 20 that he or she operates by comparing it with other water treatment plants 20.

[0023] In the following, the water treatment plant 20 will be described as a sewage treatment plant, but is not limited thereto. Specifically, the water treatment plant 20 may be, for example, a water purification plant or a pumping station. The water treatment plant 20 may also be, for example, a chemical plant or an electric plant where water to be treated is treated.

[0024] In addition, the following description will be given of a case where the water treatment system 100 has one information processing device 10, but is not limited to this. Specifically, the water treatment system 100 may have, for example, a plurality of information processing devices 10. And, the information output process may be performed in a distributed manner in two or more information processing devices 10 included in the plurality of information processing devices 10.

[0025] [Hardware configuration of information processing device 10] Next, a description will be given of the hardware configuration of the information processing device 10. Fig. 2 is a diagram illustrating the hardware configuration of the information processing device 10 in the first embodiment.

[0026] The information processing device 10 is, for example, an electronic device having an electronic circuit. Specifically, as shown in Fig. 2, the information processing device 10 is, for example, a computer device having a CPU (Central Computing Unit) 101, which is a processor, a memory 102, a communication device 103, and a storage medium 104. Each part is connected to each other via, for example, a bus 105.

[0027] The storage medium 104 has, for example, a program storage area (not shown) for storing a program 110 for performing information output processing.

[0028] The storage medium 104 also includes a storage unit 130 (hereinafter also referred to as an information storage area 130) that stores various information required for executing the information output process. Specifically, the storage medium 104 may be, for example, a hard disk drive (HDD) or a solid state drive (SSD).

[0029] The CPU 101 performs information output processing by, for example, executing 110 loaded from the storage medium 104 into the memory 102 .

[0030] The communication device 103 communicates with, for example, the water treatment plant 20 (each sensor provided in the water treatment plant 20).

[0031] The electronic circuit of the information processing device 10 may be, for example, a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC). In this case, the information output process may be executed in, for example, the FPGA or the ASIC.

[0032] [Functions of the information processing device 10] Next, a description will be given of the functions of the information processing device 10. Fig. 3 is a block diagram of the information processing device 10 in the first embodiment. Fig. 4 is a diagram for explaining information stored in the information storage area 130 in the first embodiment.

[0033] As shown in FIG. 3, the information processing device 10 realizes various functions including an information management unit 111, a data acquisition unit 112, a feature calculation unit 113, and an information output unit 114 by organic cooperation between hardware such as a CPU 101 and a memory 102 and programs.

[0034] 4, the information storage area 130 stores, for example, operation data DT0, plant information 131, equipment information 132, and characteristic information 133.

[0035] The information management unit 111 stores, for example, plant information 131 and equipment information 132 inputted to the information processing device 10 by a manager or the like in the information storage area 130. The plant information 131 is, for example, information about each water treatment plant 20. The equipment information 132 is, for example, information about each piece of equipment provided in each water treatment plant 20.

[0036] The data acquisition unit 112, for example, acquires the operating data DT0 at predetermined intervals from each water treatment plant 20. Then, the data acquisition unit 112 stores the acquired operating data DT0 in the information storage area 130, for example.

[0037] Specifically, the data acquisition unit 112 receives, for example, the operating data DT0 transmitted from each water treatment plant 20 at a predetermined interval.

[0038] In the following, a case will be described in which the BOD concentration, TN concentration, TP concentration, amount of chemicals, and amount of electricity are each included in one piece of operation data DT0 and transmitted from each water treatment plant 20, but this is not limited to the above. Specifically, for example, each of the BOD concentration, etc. may be transmitted from each water treatment plant 20 as different operation data DT0.

[0039] The characteristic calculation unit 113 calculates, for example, characteristic information 133 indicating the characteristic of the driving data DT0 acquired by the data acquisition unit 112. Then, the characteristic calculation unit 113 stores the calculated characteristic information 133 in the information storage area 130, for example.

[0040] Specifically, the characteristic calculation unit 113 classifies, for example, each of the operation data DT0 acquired by the data acquisition unit 112 according to the water treatment plant 20 that is the transmission source of each operation data DT0. Then, for example, the characteristic calculation unit 113 generates operation data DT1 by aggregating one or more operation data DT0 corresponding to each water treatment plant 20 for each water treatment plant 20. For example, the operation data DT1 may be the operation data DT0 last transmitted from each water treatment plant 20 among the one or more operation data DT0 corresponding to each water treatment plant 20. In addition, the operation data DT1 may be data generated by averaging values ​​included in the one or more operation data DT0 corresponding to each water treatment plant 20. After that, the characteristic calculation unit 113 identifies, for example, the operation data DT1 (hereinafter, also referred to as outlier data DT2) that is an outlier in the operation data DT1 for each water treatment plant 20. Then, the characteristic calculation unit 113 generates, for example, information indicating the identified outlier data DT2 as at least a part of the characteristic information 133.

[0041] More specifically, the characteristic calculation unit 113, for example, refers to the plant information 131 stored in the information storage area 130, and identifies a value (hereinafter also referred to as a first value) corresponding to each water treatment plant 20 for each water treatment plant 20. The first value is, for example, the amount of water to be treated in each water treatment plant 20 (for example, the amount of water to be treated per unit time) or a value indicating the water quality standard for the treated water discharged from each water treatment plant 20. Then, the characteristic calculation unit 113 calculates a new value (hereinafter also referred to as a third value) for each water treatment plant 20, for example, by using a value (hereinafter also referred to as a second value) included in the operation data DT1 corresponding to each water treatment plant 20 and the first value corresponding to each water treatment plant 20. The second value is, for example, a BOD concentration included in each operation data DT1. The third value is, for example, a value calculated by dividing the first value by the second value, or a value calculated by dividing the second value by the first value. Thereafter, the feature calculation unit 113, for example, compares the third values ​​for each water treatment plant 20 with each other to identify outlier data DT2 in the operation data DT1 for each water treatment plant 20.

[0042] The information output unit 114 outputs, for example, the feature information 133 generated by the feature calculation unit 113 to an operation terminal (not shown) that can be viewed by an administrator or the like.

[0043] The characteristic calculation unit 113 may generate, for example, the distribution of each of the operational data DT1 for each water treatment plant 20 as at least a part of the characteristic information 133.

[0044] Specifically, the feature calculation unit 113 may generate a distribution corresponding to each operation data DT1 by plotting points representing combinations of the first value and the second value corresponding to each water treatment plant 20 on a two-dimensional plane in which the X-axis corresponds to the first value and the Y-axis corresponds to the second value. The feature calculation unit 113 may generate a distribution corresponding to each operation data DT1 by plotting points representing combinations of the second value and the first value corresponding to each water treatment plant 20 on a two-dimensional plane in which the X-axis corresponds to the second value and the Y-axis corresponds to the first value. The feature calculation unit 113 may generate a distribution corresponding to each operation data DT1 by plotting points representing combinations of the first value and the other first value corresponding to each water treatment plant 20 on a two-dimensional plane in which the X-axis corresponds to the first value and the Y-axis corresponds to the other first value. Furthermore, the feature calculation unit 113 may generate a distribution corresponding to each of the operation data DT1 by plotting points representing combinations of the second value and other second values ​​corresponding to each of the water treatment plants 20 on a two-dimensional plane in which the X-axis corresponds to the second value and the Y-axis corresponds to the other second values. The information output unit 114 may output the distribution generated by the feature calculation unit 113 to an operation terminal, for example.

[0045] [Flowchart of information output process] Next, a description will be given of a flowchart of the information output process in the information processing device 10. Fig. 5 is a flowchart for explaining the information output process in the first embodiment. Figs. 6 to 11 are diagrams for explaining the information output process in the first embodiment.

[0046] The data acquisition unit 112 of the information processing device 10 acquires, for example, the operation data DT0 transmitted from each water treatment plant 20 (step S1 in FIG. 5). A specific example of the operation data DT0 transmitted from the water treatment plant 20 will be described below.

[0047] [Specific example of operation data DT0 (1)] Fig. 6 is a diagram for explaining a specific example of the operation data DT0 acquired from the water treatment plant 20. Specifically, Fig. 6 is a diagram for explaining a specific example of one piece of operation data DT0 transmitted from one water treatment plant 20.

[0048] The operation data DT0 shown in Fig. 6 has, for example, items of "Plant ID" in which identification information of the water treatment plant 20 is set, "Amount of chemical" in which the amount of chemical added to the water to be treated in the water treatment plant 20 is set, and "Amount of power" in which the amount of power consumed in the water treatment plant 20 is set. The operation data DT0 shown in Fig. 6 also has, for example, items of "BOD concentration" in which the BOD concentration of the water to be treated in the water treatment plant 20 is set, "TN concentration" in which the TN concentration of the water to be treated in the water treatment plant 20 is set, and "TP concentration" in which the TP concentration of the water to be treated in the water treatment plant 20 is set. The operation data DT0 shown in Fig. 6 also has, for example, an item of "Date and time" in which the date and time when the operation data DT0 was generated in the water treatment plant 20 is set.

[0049] Specifically, in the operation data DT0 shown in FIG. 6, for example, "PL01" is set as the "plant ID", "AAA1 (L)" is set as the "amount of chemical", "BBB1 (kW / h)" is set as the "amount of power", "CCC1 (mg / L)" is set as the "BOD concentration", "DDD1 (mg / L)" is set as the "TN concentration", "EEE1 (mg / L)" is set as the "TP concentration", and "4 / 1 12:00:00" is set as the "date and time".

[0050] Returning to Fig. 5, the data acquisition unit 112 of the information processing device 10, for example, identifies the type of water treatment in the water treatment plant 20 that is the transmission source of the operation data DT0 acquired in step S1 (hereinafter, also simply referred to as the water treatment type). Then, the data acquisition unit 112, for example, stores the operation data DT0 acquired in step S1 in the information storage area 130 in association with the identified water treatment type (step S2 in Fig. 5). The water treatment type may include, for example, at least one of a type indicating an anaerobic-anoxic-aerobic process, a type indicating a circulating nitrification-denitrification process, a type indicating an anaerobic-aerobic activated sludge process, and a type indicating a standard activated sludge process.

[0051] That is, the data acquisition unit 112 classifies the operating data DT0 acquired in step S1, for example, by water treatment type, and then accumulates the data in the information storage area .

[0052] Specifically, the data acquisition unit 112, for example, refers to the plant information 131 stored in the information storage area 130, and identifies the water treatment type at the water treatment plant 20 that is the transmission source of the operation data DT0 acquired in step S1. Then, the data acquisition unit 112, for example, associates the operation data DT0 acquired in step S1 with information indicating the identified water treatment type, and stores it in the information storage area 130. In addition, the data acquisition unit 112, for example, stores the operation data DT0 acquired in step S1 in the information storage area 130 in a state categorized by the identified water treatment type. A specific example of step S2 will be described below.

[0053] [Specific example of step S2] Fig. 7 and Fig. 8 are diagrams for explaining a specific example of step S2. Specifically, Fig. 7 is a diagram for explaining a specific example of the plant information 131. Also, Fig. 8 is a diagram for explaining a specific example of the operation data DT0 in a state stored in the information storage area 130.

[0054] [Specific example of Plant Information 131] First, a specific example of the plant information 131 will be described.

[0055] The plant information 131 shown in FIG. 7 has, for example, items such as a "plant ID" in which identification information for the water treatment plant 20 is set, a "management ID" in which identification information for the owner of the water treatment plant 20 (e.g., a local government) or the management entity (e.g., a management company) that operates and manages the water treatment plant 20 is set, a "water volume" in which the volume of water to be treated in the water treatment plant 20 (the volume of water to be treated that is to be treated) is set, and a "water treatment type" in which identification information for the type of water treatment in the water treatment plant 20 is set.

[0056] Specifically, in the information on the first line of the plant information 131 shown in FIG. 7, for example, “PL01” is set as the “plant ID”, “M01” is set as the “management ID”, and “100,000 (m 3 / day) is set, and "Water treatment type" is set to "T02".

[0057] In addition, in the information on the second line of the plant information 131 shown in FIG. 7, for example, “PL02” is set as the “Plant ID”, “M01” is set as the “Management ID”, and “84,000 (m 3 / day) is set, and "Water treatment type" is set to "T01".

[0058] In addition, in the information on the third line of the plant information 131 shown in FIG. 7, for example, “PL03” is set as the “Plant ID”, “M02” is set as the “Management ID”, and “32,000 (m 3 7. "Water treatment type" is set to "T02" and "Water treatment time" is set to "T03". Explanation of other information included in FIG. 7 will be omitted.

[0059] That is, in the plant information 131 shown in Fig. 7, for example, "T02" is set in the "water treatment type" of the information in the first line and the information in the third line. Therefore, the plant information 131 shown in Fig. 7 indicates that, for example, the water treatment type in the water treatment plant 20 whose "plant ID" is "PL01" is the same as the water treatment type in the water treatment plant 20 whose "plant ID" is "PL03".

[0060] [Specific example of operation data DT0 (2)] Next, a specific example of the operating data DT0 stored in the information storage area 130 will be described.

[0061] The operation data DT0 shown in FIG. 8 includes, in addition to the items included in the operation data DT0 described with reference to FIG. 6, a "water treatment type" in which the water treatment type in the water treatment plant 20 is set.

[0062] Specifically, the operating data DT0 shown in the first row of Figure 8 has, for example, "T01" set as the "water treatment type," "PL02" set as the "plant ID," "AAA5 (L)" set as the "amount of chemical," "BBB5 (kW / h)" set as the "energy amount," "CCC5 (mg / L)" set as the "BOD concentration," "DDD5 (mg / L)" set as the "TN concentration," "EEE5 (mg / L)" set as the "TP concentration," and "4 / 1 12:00:00" set as the "date and time."

[0063] In addition, in the operation data DT0 shown in the fifth row of Fig. 8, for example, "T02" is set as the "water treatment type", "PL01" is set as the "plant ID", "AAA1 (L)" is set as the "amount of chemical", "BBB1 (kW / h)" is set as the "energy amount", "CCC1 (mg / L)" is set as the "BOD concentration", "DDD1 (mg / L)" is set as the "TN concentration", "EEE1 (mg / L)" is set as the "TP concentration", and "4 / 1 12:00:00" is set as the "date and time". Description of other information included in Fig. 8 will be omitted.

[0064] That is, in the plant information 131 shown in Fig. 7, for example, in the information in which the "plant ID" is "PL01" (information in the first row), "water treatment type" is set to "T02". Therefore, for example, when the operation data DT0 described in Fig. 6 (operation data DT0 in which the "plant ID" is "PL01") is acquired in step S1, the data acquisition unit 112 associates the operation data DT0 described in Fig. 6 with information indicating that the water treatment type is "T02" and stores them in the information storage area 130, as shown in the information in the fifth row of Fig. 8.

[0065] In addition, when the operating data DT0 acquired in step S1 is categorized by water treatment type and stored in the information storage area 130, the data acquisition unit 112 may store the operating data DT0 acquired in step S1 directly in the information storage area 130, for example, without associating it with information indicating the water treatment type.

[0066] Returning to FIG. 5, the feature calculation unit 113 of the information processing device 10 generates a distribution of the operational data DT1 for each water treatment type, for example (step S3 in FIG. 5).

[0067] Specifically, the feature calculation unit 113, for example, identifies the operation data DT0 (hereinafter also referred to as target operation data DT0) corresponding to a water treatment type (hereinafter also referred to as target water treatment type) designated in advance as a water treatment type for which a distribution of operation data DT1 is to be generated, from among the operation data DT0 stored in the information storage area 130. Then, for example, for each water treatment plant 20, the feature calculation unit 113 generates operation data DT1 corresponding to each water treatment plant 20 from one or more operation data DT0 corresponding to each water treatment plant 20 among the identified target operation data DT0. After that, the feature calculation unit 113 generates, for example, a distribution of each generated operation data DT1 as a distribution corresponding to the target water treatment type.

[0068] When there are multiple target water treatment types, the feature calculation unit 113 generates, for example, a distribution corresponding to each of the multiple target water treatment types.

[0069] Then, the information output unit 114 of the information processing device 10 outputs the distribution generated in step S3 for each water treatment type as the feature information 133 (step S4 in FIG. 5). Specific examples of steps S3 and S4 will be described below.

[0070] [Specific example of step S3 and step S4] Fig. 9 to Fig. 11 are diagrams for explaining specific examples of steps S3 and S4. Specifically, Fig. 9 is a diagram for explaining a specific example of the operation data DT1. Also, Fig. 10 and Fig. 11 are diagrams for explaining a specific example of the distribution D of the operation data DT1. Below, a specific example of generating the distribution D of the operation data DT1 corresponding to one target water treatment type will be explained.

[0071] [Example of operation data DT1] First, a specific example of the driving data DT1 will be described. The driving data DT1 shown in Fig. 9 has the same items as the driving data DT0 described in Fig. 8, for example.

[0072] Specifically, "PL02" is set in each of the "Plant IDs" in the operation data DT0 shown in the first to fourth lines of Fig. 8. Therefore, as shown in the first line of Fig. 9, for example, the feature calculation unit 113 identifies the operation data DT0 in the fourth line, which has the most recent date and time set in the "Date and Time" field, among the operation data DT0 shown in the first to fourth lines of Fig. 8, as the operation data DT1 corresponding to the water treatment plant 20 whose "Plant ID" is "PL02".

[0073] Moreover, "PL01" is set in the "Plant ID" in the operation data DT0 shown in the fifth to ninth lines of Fig. 8. Therefore, as shown in the second line of Fig. 9, for example, the characteristic calculation unit 113 specifies the operation data DT0 in the ninth line, which has the most recent date and time set in the "Date and Time" field, among the operation data DT0 shown in the fifth to ninth lines of Fig. 8, as the operation data DT1 corresponding to the water treatment plant 20 whose "Plant ID" is "PL01". Descriptions of the other operation data DT1 included in Fig. 9 will be omitted.

[0074] [Specific example of distribution D of operation data DT1] Next, a specific example of the distribution D of the driving data DT1 will be described.

[0075] The characteristic calculation unit 113 refers to the plant information 131 stored in the information storage area 130, for example, and identifies, for each water treatment plant 20, a first value corresponding to each water treatment plant 20.

[0076] Specifically, in the information (first line information) in which the "Plant ID" in the plant information 131 described in FIG. 7 is "PL01", for example, "Water volume" is "100,000 (m 3 Therefore, the characteristic calculation unit 113 sets, for example, "100,000 (m 3 / day).

[0077] Next, the characteristic calculation unit 113 identifies, for example, each water treatment plant 20, a second value included in the operation data DT1 corresponding to each water treatment plant 20.

[0078] Specifically, in the operation data DT1 shown in the second row of Fig. 9, for example, "PL01" is set as the "plant ID" and "AAA4" is set as the "amount of chemical." Therefore, the feature calculation unit 113 identifies, for example, "AAA4" as the second value (amount of chemical) corresponding to the water treatment plant 20 whose "plant ID" is "PL01."

[0079] Next, as shown in Figures 10 and 11, for example, the feature calculation unit 113 generates a distribution D corresponding to each operating data DT1 by plotting points representing combinations of the first value and the second value corresponding to each water treatment plant 20 on a two-dimensional plane in which the X-axis corresponds to the first value (water volume) and the Y-axis corresponds to the second value (amount of chemical).

[0080] 10 and 11, the information output unit 114 outputs, for example, the generated distribution D as at least a part of the characteristic information 133. In addition, the information output unit 114 outputs, for example, a trend line L which is a straight line or a curve indicating a change trend for the points corresponding to each driving data DT1 as at least a part of the characteristic information 133.

[0081] This allows the administrator or the like to, for example, refer to the distribution D output from the information processing device 10 to grasp the occurrence status of the outlier data DT2 in each piece of operating data DT1.

[0082] Specifically, in the distribution D (hereinafter also referred to as distribution D1) shown in Fig. 10, for example, points corresponding to each operation data DT1 are concentrated closer to the trend line L (hereinafter also referred to as trend line L1) than in the case of the distribution D (hereinafter also referred to as distribution D2) shown in Fig. 11. Therefore, when the distribution D1 is output in step S4, the manager or the like can determine that, for example, the possibility of outlier data DT2 occurring is low and overall optimization between each water treatment plant 20 has progressed to a certain degree.

[0083] On the other hand, in the distribution D2 shown in Fig. 11, for example, the points corresponding to each piece of operation data DT1 are farther away from the trend line L (hereinafter also referred to as the trend line L2) than in the case of the distribution D1 shown in Fig. 10. Therefore, when the distribution D2 is output in step S4, the manager or the like can determine, for example, that there is a high possibility that outlier data DT2 has occurred and that the progress of the overall optimization among the water treatment plants 20 may be insufficient.

[0084] Returning to FIG. 5, for example, the feature calculation unit 113 identifies the driving data DT1 corresponding to the point corresponding to the outlier in the distribution D generated in step S3 as the outlier data DT2 (step S5 in FIG. 5).

[0085] Specifically, in the distribution D2 shown in Fig. 11, for example, a point corresponding to the driving data DT1a is plotted above the trend line L2 and farther from the trend line L2 than the other points. Also, in the distribution D2 shown in Fig. 11, for example, a point corresponding to the driving data DT1b is plotted below the trend line L2 and farther from the trend line L2 than the other points.

[0086] Therefore, for example, when the feature calculation unit 113 determines that the minimum value of the distance between the point corresponding to the driving data DT1a and the point corresponding to the other driving data DT1 is equal to or greater than a threshold value, the feature calculation unit 113 identifies the point corresponding to the driving data DT1a as the point corresponding to the outlier data DT2. Similarly, when the feature calculation unit 113 determines that the minimum value of the distance between the point corresponding to the driving data DT1b and the point corresponding to the other driving data DT1 is equal to or greater than a threshold value, the feature calculation unit 113 identifies the point corresponding to the driving data DT1b as the point corresponding to the outlier data DT2.

[0087] Furthermore, in step S3, the characteristic calculation unit 113 may use, for example, a basic unit of the amount of the drug or a cost of the drug (drug cost) as the second value instead of the amount of the drug. Furthermore, in step S3, the characteristic calculation unit 113 may use, for example, a basic unit of the amount of power or a cost of power (power cost) as the second value instead of the amount of power.

[0088] Returning to FIG. 5, the information output unit 114 outputs, for example, information indicating the outlier data DT2 identified in step S5 as the feature information 133 (step S6 in FIG. 5).

[0089] Specifically, the information output unit 114 outputs, as at least a part of the characteristic information 133, information indicating the water treatment plant 20 corresponding to the outlier data DT2 identified in step S5, for example.

[0090] As a result, by referring to the characteristic information 133 output in steps S4 and S6, for example, a manager or the like can not only determine the progress of overall optimization among each water treatment plant 20, but also identify water treatment plants 20 that need improvement in their operation and management methods, and identify water treatment plants 20 whose operation and management methods are superior to those of other water treatment plants 20.

[0091] That is, in the distribution D2 shown in Fig. 11, the water treatment plant 20 corresponding to the operation data DT1a indicates, for example, that the amount of chemicals required to treat the water to be treated per unit water volume is greater than that required by the water treatment plant 20 corresponding to the other operation data DT1. On the other hand, in the distribution D2 shown in Fig. 11, the water treatment plant 20 corresponding to the operation data DT1b indicates, for example, that the amount of chemicals required to treat the water to be treated per unit water volume is less than that required by the water treatment plant 20 corresponding to the other operation data DT1.

[0092] Therefore, in this case, the manager or the like can determine, for example, that the water treatment plant 20 corresponding to the operation data DT1a may not be operating efficiently in terms of the amount of chemicals used (there may be an excessive amount of chemicals used). Also, the manager or the like can determine, for example, that the water treatment plant 20 corresponding to the operation data DT1b may be operating efficiently in terms of the amount of chemicals used.

[0093] Therefore, in this case, the manager can review and change the operation management methods of the other water treatment plants 20 by, for example, analyzing the operation management method of the water treatment plant 20 corresponding to the operation data DT1b. Specifically, the manager can review and change the operation management method of each water treatment plant 20 so that, for example, the points constituting the distribution D2 are concentrated near the trend line L2. In addition, the manager can review and change the operation management method of the water treatment plant 20 corresponding to the operation data DT1a so that, for example, the points corresponding to the operation data DT1a move near the trend line L2. In addition, the manager can review and change the operation management method of each water treatment plant 20 so that, for example, the slope of the trend line L2 becomes gentler.

[0094] In this manner, the information processing device 10 in this embodiment acquires, for example, operation data DT0 corresponding to each of the multiple water treatment plants 20 from the multiple water treatment plants 20. Then, the information processing device 10 calculates, for example, characteristic information 133 indicating the characteristics of each of the operation data DT1 generated from the acquired operation data DT0. After that, the information processing device 10 outputs, for example, the calculated characteristic information 133.

[0095] Specifically, the information processing device 10 generates a distribution D for each of the driving data DT1, and identifies the generated distribution D as the feature information 133, for example.

[0096] In addition, the information processing device 10 identifies outlier data DT2 that is an outlier in the driving data DT1 based on, for example, each comparison result of the driving data DT1, and identifies information indicating the identified outlier data DT2 as feature information 133.

[0097] Furthermore, the multiple water treatment plants 20 include, for example, multiple water treatment plants 20 owned by different entities.

[0098] More specifically, the information processing device 10, for example, acquires the operation data DT0 for each type of water treatment in the water treatment plant 20 corresponding to each operation data DT0. Then, the information processing device 10 calculates, for example, for each type of water treatment, characteristic information 133 corresponding to each type based on the operation data DT1 corresponding to each type.

[0099] That is, the information processing device 10 in this embodiment outputs, for example, characteristics of the interrelationships of each of the operation data DT transmitted from each of a plurality of water treatment plants 20 including water treatment plants 20 owned by different entities, in a state in which a manager or the like can grasp in a centralized manner. In other words, the information processing device 10 outputs, for example, characteristics of the operation data transmitted from a wide variety of water treatment plants 20 having different operation management methods, treatment capacities for the water to be treated, installation areas, etc., in a state in which a manager or the like can grasp in a centralized manner.

[0100] This enables the information processing device 10 in the present embodiment to output, for example, information (characteristic information) that serves as a basis for determining the degree to which overall optimization has been achieved in a plurality of water treatment plants 20.

[0101] Therefore, for example, even if an operation management method is considered preferable in each water treatment plant 20, the manager etc. can find an improvement point that the manager etc. did not recognize by comparing it with the operation management method in other water treatment plants 20. Therefore, the manager etc. can efficiently improve the operation management method in each water treatment plant 20, for example, and can proceed with overall optimization of the multiple water treatment plants 20.

[0102] The information processing device 10 may perform steps S5 and S6 only when, for example, an administrator inputs information to the information processing device 10 to perform steps S5 and S6 during the execution of the information output process. That is, the administrator may, for example, refer to the distribution D output in step S4 and determine whether or not outlier data DT2 exists. Then, for example, when it is determined that outlier data DT2 exists in the distribution D and further identification of the outlier data DT2 is required, the administrator may input information to the information processing device 10 to perform steps S5 and S6. On the other hand, for example, when it is determined that outlier data DT2 does not exist in the distribution D and further identification of the outlier data DT2 is not required, the administrator may input information to the information processing device 10 to the effect that steps S5 and S6 are not performed. In other words, the information processing device 10 may perform steps S1 to S4 as the information output process.

[0103] Furthermore, the information processing device 10 may perform steps S3 and S4 only when, for example, an administrator or the like inputs information to the information processing device 10 indicating that steps S3 and S4 are to be performed when the information output process is being executed.

[0104] In the above example, the information processing device 10 identifies the outlier data DT2 by using the trend line L, but the present invention is not limited to this. Specifically, the information processing device 10 may identify the outlier data DT2 by, for example, clustering each piece of driving data DT1. In addition, the information processing device 10 may identify the outlier data DT2 by, for example, using a machine learning model capable of outputting the outlier data DT2 included in each piece of driving data DT1.

[0105] [Water treatment system 100 in the first modified example] Next, a modified example of the water treatment system 100 in the first embodiment (hereinafter, also referred to as a first modified example) will be described. Fig. 12 is a diagram for explaining the first modified example. Specifically, Fig. 12 is a diagram for explaining a specific example of facility information 132.

[0106] In step S3, for example, the feature calculation unit 113 may refer to the equipment information 132 stored in the information storage area 130 and identify a first value corresponding to each water treatment plant 20 for each water treatment plant 20. Specifically, in this case, the feature calculation unit 113 may use, for example, the number of pieces of equipment, such as water level gauges and pumps, provided in each water treatment plant 20 as the first value. Specific examples of the equipment information 132 will be described below.

[0107] [Example of Facility Information 132] The equipment information 132 shown in Figure 12 has, for example, items such as an "equipment ID" in which identification information of the equipment installed in the water treatment plant 20 is set, an "equipment name" in which the equipment name of the equipment installed in the water treatment plant 20 is set, a "facility ID" in which identification information of the facility in which the equipment is installed (such as a primary sedimentation tank) is set, and a "plant ID" in which identification information of the water treatment plant 20 is set.

[0108] Specifically, in the information in the first line of the equipment information 132 shown in FIG. 12, for example, "Equipment ID" is set to "E101," "Equipment Name" is set to "Pump a," "Facility ID" is set to "FA01," and "Plant ID" is set to "PL01."

[0109] In addition, in the information in the second row of the equipment information 132 shown in Fig. 12, for example, "E102" is set as the "equipment ID", "pump b" is set as the "equipment name", "FA01" is set as the "facility ID", and "PL01" is set as the "plant ID". Description of other information included in Fig. 12 will be omitted.

[0110] That is, in the facility information 132 shown in Fig. 12, for example, "PL01" is set as the "Plant ID" for each of the information in the first to fourth rows. In other words, the facility information 132 shown in Fig. 12 indicates that, for example, the number of facilities in the water treatment plant 20 whose "Plant ID" is "PL01" is 4. Therefore, in step S3, the feature calculation unit 113 may specify, for example, 4 as the first value (the number of facilities) corresponding to the water treatment plant 20 whose "Plant ID" is "PL01".

[0111] As a result, the information processing device 10 in this modification can generate the distribution D of the operating data DT1 even when using information included in the facility information 132, for example.

[0112] [Water treatment system 100 in the second modified example] Next, a modification of the water treatment system 100 in the second embodiment (hereinafter also referred to as the second modification) will be described.

[0113] In step S2, the data acquisition unit 112 may classify the operation data DT0 acquired in step S1 by water treatment type and by acquisition condition of the operation data DT0, for example, and store the classified data in the information storage area 130. In steps S3 and S4, the feature calculation unit 113 may generate and output the distribution D of the operation data DT1 by water treatment type and by acquisition condition of the operation data DT0. Furthermore, in steps S5 and S6, the feature calculation unit 113 may identify and output the outlier data DT2 by water treatment type and by acquisition condition of the operation data DT0, for example.

[0114] Specifically, the characteristic calculation unit 113 may use, for example, the installation location (installation area) of the water treatment plant 20 that is the transmission source of the operation data DT0 as an acquisition condition for the operation data DT0. The characteristic calculation unit 113 may generate the distribution D of the operation data DT1 and identify the outlier data DT2 for each water treatment type and for each installation location of the water treatment plant 20. The characteristic calculation unit 113 may also use, for example, the amount of water to be treated in the water treatment plant 20 that is the transmission source of the operation data DT0 and the generation period of the operation data DT0 as an acquisition condition for the operation data DT0.

[0115] In this manner, the information processing device 10 in this modification acquires the operation data DT0, for example, for each type of water treatment in the water treatment plant 20 corresponding to each operation data DT0 and for each acquisition condition of each operation data DT0. Then, the information processing device 10 calculates, for example, for each type of water treatment and for each acquisition condition, characteristic information 133 corresponding to each type and each acquisition condition using the operation data DT1 corresponding to each type and each acquisition condition.

[0116] This allows the information processing device 10 in this modification to, for example, generate the distribution D of the operation data DT1 and identify the outlier data DT2 using the more subdivided operation data DT0. Therefore, the manager or the like can, for example, refer to the information output from the information processing device 10 to advance overall optimization between the water treatment plants 20 that have similar operating environments. Therefore, the manager or the like can, for example, efficiently advance overall optimization between the water treatment plants 20. [Explanation of symbols]

[0117] 10: Information processing device 20: Water treatment plant 100: Water treatment system 101: CPU 102: Memory 103: Communication device 104: Storage medium 105: Bus 110: Program 111: Information Management Department 112: Data acquisition unit 113: Feature calculation unit 114: Information output unit 130: Information storage area 131: Plant information 132: Facility information 133:Characteristic information D:Distribution DT0: Operation data DT1: Operation data DT1a: Operation data DT1b: Operation data DT2: Outlier data L: Trend line L1: Trend line L2: Trend line NW: Network

Claims

1. a data acquisition unit that acquires operation data corresponding to each of the plurality of plants from the plurality of plants; A feature calculation unit that calculates feature information indicating features regarding the interrelationships of each of the acquired driving data; and an information output unit that outputs the calculated feature information.

2. The feature calculation unit is generating a distribution for each of said operational data; The information processing apparatus according to claim 1 , wherein the generated distribution is identified as the feature information.

3. The feature calculation unit is Identifying outlier data that is an outlier in the operational data based on each comparison result of the operational data; The information processing apparatus according to claim 1 , wherein information indicating the identified outlier data is identified as the feature information.

4. The information processing apparatus according to claim 1 , wherein the plurality of plants includes a plurality of plants having different owners.

5. Each of the plurality of plants is a plant that performs water treatment, The data acquisition unit acquires the operation data for each type of water treatment in a plant corresponding to each operation data, The information processing device according to claim 1 , wherein the feature calculation unit calculates the feature information corresponding to each type based on the driving data corresponding to each type.

6. The data acquisition unit acquires the operating data for each type of water treatment in a plant corresponding to each operating data and for each acquisition condition of each operating data; The information processing device according to claim 5 , wherein the characteristic calculation unit calculates, for each type of water treatment and for each acquisition condition, the characteristic information corresponding to each type and each acquisition condition using the operating data corresponding to each type and each acquisition condition.

7. An information processing device; A water treatment system having a plurality of sensors provided in each of a plurality of plants and capable of communicating with the information processing device, The information processing device includes: acquiring operation data corresponding to each of the plurality of plants from the plurality of sensors; Calculating characteristic information indicating characteristics of interrelationships between the acquired driving data; The water treatment system outputs the calculated characteristic information.

8. acquiring operation data corresponding to each of the plurality of plants from the plurality of plants; Calculating characteristic information indicating characteristics of interrelationships between the acquired driving data; The information processing method includes a process in which the calculated feature information is output by a computer.

9. acquiring operation data corresponding to each of the plurality of plants from the plurality of plants; Calculating characteristic information indicating characteristics of interrelationships between the acquired driving data; An information processing program that causes a computer to execute a process of outputting the calculated characteristic information.

Images