Countermeasure support system, countermeasure support method, and countermeasure support program

Abstract

To efficiently implement countermeasures against trouble caused by underwater particles.SOLUTION: An underwater particle countermeasure support system 100-1 includes an underwater particle image analyzer D3 and an underwater particle countermeasure support device D5. The underwater particle image analyzer D3 photographs collected measurement water, obtains image data of the measurement water, and measures underwater particles contained in the measurement water based on the image data of the measurement water. The underwater particle countermeasure support device D5 outputs analysis results used for countermeasures against trouble caused by underwater particles based on measurement results of the underwater particles transmitted from the underwater particle image analyzer D3.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a countermeasure support system and a countermeasure support method. [Background technology]

[0002] Dams, lakes, marshes, and rivers (referred to as "dams, etc." where appropriate) account for a large proportion of water sources for drinking water supplies. In the above water sources, various algae can occur due to changes in weather and water temperature, and changes over time in dams, etc., which can cause problems with algae during water purification treatment for drinking water supplies, such as coagulation inhibition, filter blockage, leakage into filtered water (filter leakage), and unpleasant odors and tastes.

[0003] Plankton surveys are conducted to implement measures against the above-mentioned algae damage (referred to as "algae countermeasures" where appropriate). The person conducting the plankton survey collects test water at the water sampling point or with a water sampling pump, prepares the sample using static sedimentation, centrifugal sedimentation, a sediment chamber, etc., and then identifies and counts the algae species using a microscope. The person conducting the survey then organizes the data of the analysis results to be used in algae countermeasures, including the measurement results of species identification and counting, the results of sensory tests, and the concentration of odorous components. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] US Patent Application Publication No. 2013 / 0315447 Summary of the Invention [Problem to be solved by the invention]

[0005] However, it is difficult to efficiently implement measures against problems caused by underwater particles such as algae contained in the test water collected from the water supply source (referred to as "underwater particle measures" as appropriate). For example, plankton surveys using a microscope require a high level of specialized knowledge and a large number of man-hours, and it is difficult to quickly reflect the analysis results in operational support and control.

[0006] The present invention has been made in consideration of the above, and has an object to efficiently implement measures against damage caused by underwater particles. [Means for solving the problem]

[0007] The present invention provides a countermeasure support system comprising an image analysis device and a countermeasure support device, wherein the image analysis device comprises an imaging unit that photographs collected test water and acquires image data of the test water, and a measurement unit that measures underwater particles contained in the test water based on the image data of the test water, and the countermeasure support device comprises an analysis unit that outputs analysis results to be used for countermeasures against problems caused by the underwater particles based on the measurement results of the underwater particles transmitted from the image analysis device.

[0008] The present invention also provides a countermeasure support method in a countermeasure support system including an image analysis device and a countermeasure support device, in which the image analysis device photographs collected test water, obtains image data of the test water, and measures underwater particles contained in the test water based on the image data of the test water, and the countermeasure support device outputs analysis results to be used for countermeasures against problems caused by the underwater particles based on the measurement results of the underwater particles transmitted from the image analysis device. Effect of the Invention

[0009] According to the present invention, it is possible to efficiently implement measures against damage caused by underwater particles. [Brief description of the drawings]

[0010] [Figure 1] 1 is a diagram showing a configuration example and a processing example of an underwater particle countermeasure support system according to a first embodiment. [Diagram 2] FIG. 1 is a diagram for explaining an outline 1 of an underwater particle countermeasure support system according to a reference technique. [Diagram 3] FIG. 2 is a diagram for explaining an outline 2 of an underwater particle countermeasure support system according to a reference technique. [Figure 4] FIG. 3 is a diagram for explaining an outline 3 of an underwater particle countermeasure support system according to a reference technique. [Diagram 5] 1 is a diagram showing a specific example of an outline of an underwater particle countermeasure support system according to a first embodiment. [Figure 6] 1 is a block diagram showing an example of the configuration of each device of an underwater particle countermeasure support system according to a first embodiment. [Figure 7] 4 is a diagram showing an example of underwater particle data in an underwater particle data server according to the first embodiment. FIG. [Figure 8] FIG. 2 is a diagram showing an example of an image library of an underwater particle data server according to the first embodiment. [Figure 9] 4 is a diagram showing an example of an algae countermeasure document for the underwater particle countermeasure support device according to the first embodiment. FIG. [Figure 10] FIG. 3 is a diagram showing an example of a measurement point graph of the underwater particle countermeasure support device according to the first embodiment. [Figure 11] FIG. 2 is a diagram showing an example of a time-series trend graph of the underwater particle countermeasure support device according to the first embodiment. [Figure 12] 5 is a flowchart showing an example of a process flow of the underwater particle countermeasure support system according to the first embodiment. [Figure 13] FIG. 2 is a diagram illustrating an example of a hardware configuration according to the first embodiment. [Figure 14] 11 is a diagram showing a configuration example and a processing example of an underwater particle countermeasure support system according to a second embodiment. FIG. [Figure 15] FIG. 11 is a diagram showing an example of an odorant assumed concentration graph of the underwater particle countermeasure support device according to the second embodiment. [Figure 16] 11 is a diagram showing a configuration example and a processing example of an underwater particle countermeasure support system according to a third embodiment. FIG. [Figure 17] FIG. 11 is a diagram showing an example of a coagulation-sedimentation simulation process of the underwater particle countermeasure support device according to the third embodiment. [Figure 18] 13 is a diagram showing a configuration example and a processing example of an underwater particle countermeasure support system according to a fourth embodiment. FIG. [Figure 19]FIG. 13 is a diagram showing an example of a filtration blockage simulation in the filtration simulation process of the underwater particle countermeasure support device according to the fourth embodiment. [Figure 20] FIG. 13 is a diagram showing an example of a filtration leakage simulation in the filtration simulation process of the underwater particle countermeasure support device according to the fourth embodiment. [Figure 21] FIG. 13 is a diagram showing an example of a filtration blockage prediction graph of the underwater particle countermeasure support device according to the fourth embodiment. [Figure 22] FIG. 13 is a diagram showing an example of a filtration leakage prediction graph of the underwater particle countermeasure support device according to the fourth embodiment. [Figure 23] FIG. 13 is a diagram showing an example of an off-odor taste / odor prediction graph of the underwater particle countermeasure support device according to the fourth embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, a countermeasure support system and a countermeasure support method according to an embodiment of the present invention will be described in detail with reference to the drawings. Note that the present invention is not limited to the embodiment described below.

[0012] [Embodiment 1] Below, we will explain the configuration and processing of underwater particle countermeasure support system 100-1 according to embodiment 1, the configuration and processing of each device of underwater particle countermeasure support system 100-1, and the processing flow of underwater particle countermeasure support system 100-1, and finally the effects of embodiment 1.

[0013] 1. Configuration and Processing of Underwater Particle Countermeasure Support System 100-1 The configuration and processing of underwater particle countermeasure support system 100-1 according to embodiment 1 will be described in detail with reference to Fig. 1. Fig. 1 is a diagram showing an example of the configuration and processing of underwater particle countermeasure support system 100-1 according to embodiment 1. Below, an example of the overall configuration of underwater particle countermeasure support system 100-1 and an example of the overall processing of underwater particle countermeasure support system 100-1 will be described in order, and finally, the effects of underwater particle countermeasure support system 100-1 will be described.

[0014] In the first embodiment, water purification treatment in a water purification facility of a water supply system is described as an example, but the field of use is not limited thereto, and the present invention can also be applied to water treatment in sewage systems and industrial wastewater, monitoring of river water, lake water, and ocean water, and monitoring of agricultural water and industrial water, etc.

[0015] (1-1. Example of the overall configuration of the underwater particle countermeasure support system 100-1) An example of the overall configuration of the underwater particle countermeasure support system 100-1 will be described with reference to FIG. 1. The underwater particle countermeasure support system 100-1 includes a monitoring control device D1, a sampling device D2, an underwater particle image analyzer D3, an underwater particle data server D4, an underwater particle countermeasure support device D5, and a display terminal D6. The monitoring control device D1, the underwater particle image analyzer D3, the underwater particle data server D4, the underwater particle countermeasure support device D5, and the display terminal D6 are connected to each other via a predetermined communication network (not shown) so as to be able to communicate with each other by wire or wirelessly. The predetermined communication network may be any of various communication networks such as the Internet or a dedicated line. The sampling device D2 and the underwater particle image analyzer D3 are connected to each other by a pipe that supplies the collected measurement water W.

[0016] (1-1-1. Monitoring and control device D1) The monitoring and controlling device D1 is a control device that executes monitoring and controlling processing in the water purification facility. Note that the underwater particle countermeasure support system 100-1 shown in Fig. 1 may include multiple monitoring and controlling devices D1. The monitoring and controlling device D1 may be realized in a cloud environment, an on-premise environment, an edge environment, or the like.

[0017] (1-1-2. Sampling device D2) The sampling device D2 is a sampling device that samples the test water W in the water purification facility. The underwater particle countermeasure support system 100-1 shown in Fig. 1 may include a plurality of sampling devices D2. The sampling device D2 is installed in a water intake facility such as a water purification facility or a dam.

[0018] (1-1-3. Underwater particle image analysis device D3) The underwater particle image analyzer D3 is an image analyzer that analyzes image data I of the measurement water W. The underwater particle countermeasure support system 100-1 shown in Fig. 1 may include a plurality of underwater particle image analyzers D3. The underwater particle image analyzers D3 are installed in water purification facilities, water intake facilities such as dams, analysis centers, etc.

[0019] (1-1-4. Underwater particle data server D4) The underwater particle data server D4 is a server device that stores various data related to the underwater particles P contained in the measurement water W. Note that the underwater particle countermeasure support system 100-1 shown in Fig. 1 may include multiple underwater particle data servers D4. In the example of Fig. 1, the underwater particle data server D4 is installed in a cloud environment, but it may be realized in an on-premise environment, an edge environment, or the like.

[0020] (1-1-5. Underwater particle countermeasure support device D5) The underwater particle countermeasure support device D5 is a countermeasure support device that outputs various data that supports countermeasures against problems caused by underwater particles P. Note that the underwater particle countermeasure support system 100-1 shown in Fig. 1 may include multiple underwater particle countermeasure support devices D5. Also, in the example of Fig. 1, the underwater particle countermeasure support device D5 is installed in a cloud environment, but it may also be realized in an on-premise environment, an edge environment, or the like.

[0021] (1-1-6. Display terminal D6) The display terminal D6 is a manager terminal used by an operator O who is the manager of the water purification facility. Note that the underwater particle countermeasure support system 100-1 shown in FIG. 1 may include a plurality of display terminals D6.

[0022] (1-2. Example of overall processing of underwater particle countermeasure support system 100-1) An example of the overall processing of the underwater particle countermeasure support system 100-1 will be described with reference to Fig. 1. Note that the following steps (1) to (13) of processing can be executed in a different order. Also, some of the following steps (1) to (13) of processing can be omitted.

[0023] (1-2-1. Measurement water collection process) First, the sampling device D2 collects the measurement water W (see FIG. 1(1)). For example, the sampling device D2 collects dam intake water W1 from a water source P1, such as a dam including a water intake facility. The sampling device D2 also collects raw water W2 sent from the water source P1 to a receiving well P2. The sampling device D2 also collects submerged water W3 sent from the settling basin P5 to the rapid sand filter basin P6. The sampling device D2 also collects filtered water W4 sent from the rapid sand filter basin P6 to the purified water reservoir P7. The sampling device D2 also collects purified water W5 sent from the purified water reservoir P7 to the water distribution facility.

[0024] (1-2-2. Measurement water photography processing) Secondly, the underwater particle image analysis device D3 photographs the measurement water W (see FIG. 1(2)). For example, the underwater particle image analysis device D3 photographs the measurement water W supplied from the sampling device D2 through a pipe, and obtains image data I of the sampled measurement water W.

[0025] (1-2-3. Image data transmission process) Thirdly, the underwater particle image analysis device D3 transmits (see FIG. 1(3)) the image data I of the measurement water W. For example, the underwater particle image analysis device D3 transmits the acquired image data I of the measurement water W to the underwater particle data server D4.

[0026] (1-2-4. Underwater particle identification processing) Fourth, the underwater particle data server D4 identifies the underwater particles P (see FIG. 1(4)). For example, the underwater particle data server D4 performs image analysis of the image data I of the measurement water W transmitted from the underwater particle image analyzer D3, and identifies the types of the underwater particles P contained in the measurement water W.

[0027] Here, the underwater particles P are particles of about 2 μm to 1 mm contained in the measurement water W, and include various algae as well as pollen, silt, microplastics, etc. The underwater particles P may be particles of about 300 nm to 5 mm.

[0028] (1-2-5. Image library transmission process) Fifth, the underwater particle data server D4 transmits the image library L to the underwater particle image analysis device D3 (see FIG. 1(5)). For example, the underwater particle data server D4 transmits the image library L corresponding to the identified underwater particle P to the underwater particle image analysis device D3.

[0029] Here, the image library L refers to multiple image data indicating the shape, color, and size of the underwater particles P, which are associated with each type of underwater particle P, and is data used for machine learning of the classification model D34 that classifies the underwater particles P, and for statistical analysis when classifying the underwater particles P.

[0030] (1-2-6. Underwater particle characteristics transmission processing) Sixth, the underwater particle data server D4 transmits the underwater particle characteristics to the underwater particle countermeasure support device D5 (see FIG. 1(6)). For example, the underwater particle data server D4 transmits the underwater particle characteristics corresponding to the identified underwater particle P to the underwater particle countermeasure support device D5.

[0031] Here, the underwater particle characteristics refer to substances that cause impediments in water purification treatment that are generated by the underwater particles P, the concentration of the substances per particle of the underwater particles P, the specific gravity of the underwater particles P, etc., which are associated with each type of underwater particle P.

[0032] (1-2-7. Underwater particle measurement processing) Seventh, the underwater particle image analyzer D3 measures the underwater particles P (see FIG. 1(7)). For example, the underwater particle image analyzer D3 executes machine learning of the classification model D34 using the image library L transmitted from the underwater particle data server D4, classifies the underwater particles P in the image data I using the trained classification model D34, and measures the number of particles for each type of the classified underwater particles P.

[0033] In addition, the underwater particle image analysis device D3 uses the image library L transmitted from the underwater particle data server D4 to perform statistical analysis of the underwater particles P using a classification model D34, classifies the underwater particles P in the image data I using the extracted features of the underwater particles P, and measures the number of particles of each type of classified underwater particles P.

[0034] The underwater particle image analyzer D3 can also calculate feature quantities of the underwater particles P in the image data I and output a particle size distribution. For example, the underwater particle image analyzer D3 can calculate particle diameter, length and width, aspect ratio, chromaticity, etc. as feature quantities of the underwater particles P in the image data I, measure the number of particles for each particle diameter, the number of particles for each length and width, the number of particles for each aspect ratio, the number of particles for each chromaticity, etc., and output them as a particle size distribution.

[0035] (1-2-8. Measurement result transmission process) Eighth, the underwater particle image analyzer D3 transmits the measurement results M to the underwater particle countermeasures support device D5 (see FIG. 1(8)). For example, the underwater particle image analyzer D3 transmits the number of particles for each type of classified underwater particles P as the measurement results M to the underwater particle countermeasures support device D5.

[0036] (1-2-9. Underwater particle analysis processing) Ninth, the underwater particle countermeasure support device D5 analyzes the underwater particles P (see FIG. 1(9)). For example, the underwater particle countermeasure support device D5 outputs, as the analysis result A, an underwater particle countermeasure form A1 including the measurement results M of the underwater particles P transmitted from the underwater particle image analyzer D3, the underwater particle characteristics transmitted from the underwater particle data server D4, and the fault classification corresponding to the underwater particle characteristics.

[0037] At this time, the underwater particle countermeasure support device D5 may output, as the analysis result A, a measurement point graph A2 including a graph showing the measurement result M for each water sampling point of the test water W, a graph showing the degree of occurrence of a problem for each water sampling point of the test water W, etc. Furthermore, the underwater particle countermeasure support device D5 may output, as the analysis result A, a time-series trend graph A3 including a graph showing the measurement result M for each measurement date of the test water W, a graph showing the degree of occurrence of a problem for each measurement date of the test water W, etc.

[0038] (1-2-10. Analysis result transmission process) Tenth, the underwater particle countermeasure support device D5 transmits the analysis result A to the display terminal D6 (see FIG. 1 (10)). For example, the underwater particle countermeasure support device D5 transmits, as the analysis result A, an underwater particle countermeasure form A1, a measurement location graph A2, a time series trend graph A3, and the like to the display terminal D6.

[0039] (1-2-11. Analysis result display processing) Eleventh, the display terminal D6 displays the analysis results A (see FIG. 1(11)). For example, the display terminal D6 displays, as the analysis results A, an underwater particle countermeasures form A1, a measurement location graph A2, a time series trend graph A3, etc. on a monitor screen.

[0040] (1-2-12. Input processing for underwater particle countermeasures) Twelfth, the operator O inputs underwater particle countermeasures to the monitoring and control device D1 (see FIG. 1 (12)). For example, the operator O inputs underwater particle countermeasures such as powdered activated carbon injection, pH adjuster injection, and coagulant injection to the monitoring and control device D1 based on the analysis result A displayed on the monitor screen of the display terminal D6.

[0041] (1-2-13. Underwater particle countermeasure execution process) Thirteenth, the monitoring and control device D1 implements measures against underwater particles (see FIG. 1 (13)). For example, the monitoring and control device D1 implements measures against underwater particles in the water purification facility by optimally executing powdered activated carbon injection control of the powdered activated carbon injection facility, pH adjuster injection control of the pH adjuster injection facility, coagulant injection control of the coagulant injection facility, and filtration facility control of the filtration facility.

[0042] (1-3. Effects of the Underwater Particle Countermeasures Support System 100-1) In the following, an overview and problems of the underwater particle countermeasure support system 100P according to the reference technology will be described, and then the effects of the underwater particle countermeasure support system 100-1 will be described.

[0043] (1-3-1. Overview of the Underwater Particle Countermeasures Support System 100P) An overview of underwater particle countermeasure support system 100P according to the reference technology will be described with reference to Figures 2 to 4. Figures 2 to 4 are diagrams for explaining an overview of underwater particle countermeasure support system 100P according to the reference technology. Below, an overview 1 regarding the configuration and processing of underwater particle countermeasure support system 100P, an overview 2 regarding the processing flow of underwater particle countermeasure support system 100P, and an overview 3 regarding analysis result A created by underwater particle countermeasure support system 100P will be described.

[0044] (1-3-1-1. Overview 1) An overview 1 of the configuration and processing of the underwater particle countermeasure support system 100P will be described using Figure 2. Below, a water purification facility that performs rapid filtration and powdered activated carbon processing will be described. Note that in large-scale water purification facilities, ozone processing, granular activated carbon processing, etc. may also be performed.

[0045] In the underwater particle countermeasures support system 100P, the water purification equipment is composed of various equipment such as a water source P1 such as a dam including a water intake facility, a receiving well P2, a chemical mixing basin P3, a flocculation basin P4, a sedimentation basin P5, a rapid filtration basin P6 including a filtration basin facility, a purified water basin P7, a wastewater treatment basin P8, a powdered activated carbon injection equipment including powdered activated carbon P9, a pH adjuster injection equipment including a pH adjuster P10, and a flocculant injection equipment including a flocculant P11, and a monitoring and control device D1 that monitors and controls the various equipment.

[0046] In the underwater particle countermeasures support system 100P, the person who conducts plankton surveys and algae countermeasures performs the operations of water sampling (F1), sample preparation (F2), species identification and counting (F3), data organization (F4), judgment (F5), reference material (F6), and manual input (F7), as described in detail in (1-3-1-2. Overview 2).

[0047] In addition, as part of the above water sampling (F1) operation, the implementer collects raw water W2 sent from the water source P1 to the receiving well P2 (see "*a"). The implementer also collects submerged water W3 sent from the settling basin P5 to the rapid sand filter basin P6 (see "*b"). The implementer also collects filtered water W4 sent from the rapid sand filter basin P6 to the clear water basin P7 (see "*c").

[0048] The implementer also measures the water quality (turbidity, pH, alkalinity, water temperature) S1 of the raw water W2 at the receiving well P2. At this time, the implementer performs a sensory test (odor test) and GC / MS (gas chromatography mass spectrometry) to detect odorous substances (2-methylisoborneol, geosmin, etc.), and a biological survey (mainly algae) using a microscope, and if a certain amount of odor, odorous substances, or odor-causing algae is detected, powdered activated carbon P9 is injected from the powdered activated carbon injection equipment. The implementer also injects a pH adjuster P10 such as caustic soda or sulfuric acid from the pH adjuster injection equipment to adjust the pH to a level suitable for coagulation.

[0049] The implementer also injects a coagulant P11, such as PAC (polyaluminum chloride) or aluminum sulfate, into the chemical mixing tank P3 and rapidly stirs the water. At this time, the implementer calculates the coagulant injection rate based on the raw water turbidity S1. Since the water properties differ depending on the water purification facility, the implementer derives a relational equation from experiments and experience. Since the optimal injection rate varies depending on the season and weather conditions, the implementer also performs a jar test (injection test of coagulant and pH adjuster using a beaker) to determine the injection rate.

[0050] In addition, the operator grows the turbid matter into large flocs by slow stirring in the flocculation pond P4. At this time, the operator detects the state of the flocs with a sensor and uses it for feedback control.

[0051] In addition, the operator removes the large grown flocs by settling in a settling tank P5. Here, the settling velocity v sAccording to Stokes' law, is proportional to the square of the particle diameter and the density difference with water. At this time, the operator measures the turbidity S2 at the outlet of the settling tank and utilizes it for feedback control.

[0052] The implementer also performs sand filtration of the supernatant water from the settling basin P5 in the rapid filtration basin P6 to further remove suspended solids. Here, the implementer performs cleaning (surface washing, backwashing) periodically or when the filtration resistance increases, to maintain the filtration capacity. At this time, the implementer sends the cleaning wastewater to the drainage basin P8 and returns it to the receiving well P2. Note that the implementer controls the return flow rate to keep it as constant as possible, since the return flow rate is a factor that affects the flocculation and sedimentation processes. The implementer also sends the filtered water W4 to the clean water basin P7, where it is disinfected with chlorine or the like, and then sent to the water distribution facility.

[0053] (1-3-1-2. Overview 2) Overview 2 of the process flow of the underwater particle countermeasures support system 100P will be explained using Fig. 3. Below, the steps of the plankton survey, water sampling (F1), sample preparation (F2), species identification and counting (F3), data reduction (F4), judgment (F5), reference material (F6), and manual input (F7), will be explained.

[0054] (Water collection) First, the person conducting the plankton survey performs water sampling (F1). At this time, the person conducting the survey collects test water W at each water sampling point or water sampling pump.

[0055] (Sample Preparation) Second, the person conducting the plankton survey prepares the sample (F2). At this time, the person prepares the collected measurement water W by the static sedimentation method, the centrifugal sedimentation method, a sediment chamber, or the like.

[0056] (Species identification and counting) Third, the plankton surveyor performs a species identification and count (F3), in which the surveyor uses a microscope to identify algal species and measure the number of particles of each identified species.

[0057] (Data Reduction) Fourth, the person conducting the plankton survey performs data sorting (F4). At this time, the person conducting the survey creates analysis results A, which include the measurement results M for each identified species, the sensory test results, and the concentration of odorous components.

[0058] (Judgment) Fifth, the person conducting the plankton survey makes a decision (F5). At this time, the person conducting the survey determines the injection rate of powdered activated carbon, etc., based on indexes and relational expressions that have been determined in advance through experiments, etc.

[0059] (Reference material) Sixth, plankton surveyors use the reference materials (F6). For example, when identifying and counting the above species (F3), surveyors must fully understand the reference contents and important points written in the reference materials (F6). In addition, when making the above judgments (F5), surveyors must refer to the indices and relational expressions written in the reference materials (F6).

[0060] (Manual Entry) Seventh, the person conducting the plankton survey performs manual input (F7). At this time, the person conducting the survey changes the settings such as the injection rate of powdered activated carbon, thereby reflecting the changes in the operation of the monitoring and controlling device D1.

[0061] (1-3-1-3. Overview 3) An overview 3 of the analysis results A created by the underwater particle countermeasures support system 100P will be described with reference to Fig. 4. Below, the classification and name, the measurement results M, the sensory test results, and the odor components will be described.

[0062] (Classification and name) As shown in “Classification” and “Name” in Figure 4, the person conducting the plankton survey identifies underwater particles P such as algae using the above-mentioned reference material (F6) in the above-mentioned species identification and counting (F3), and records the classification and name of the underwater particles P as analysis result A.

[0063] (Measurement result M) As shown in Figure 4 for "Dam intake water," "Raw water," "Submerged water," "Filtered water," and "Purified water," the person conducting the plankton survey measures the number of particles in the dam intake water W1, raw water W2, submerged water W3, filtered water W4, and purified water W5, which are the test water W collected in the above-mentioned water sampling (F1), in the above-mentioned species identification and counting (F3), and records the measurement result M, which is the number of particles per mL of test water W, as the analysis result A.

[0064] (Sensory test results) As shown in “Sensory Test” in Figure 4, the person conducting the plankton survey conducts a sensory test and records the sensory odor and odor intensity of the test water W as the analysis result A.

[0065] (Odor components) As shown in “Odor Components” in Figure 4, the person conducting the plankton survey performs GC / MS and records the concentration (mass per particle) of odorous substances 2-methylisoborneol (2-MIB) and geosmin contained in the measurement water W as analysis result A.

[0066] (others) As shown in "Disturbance" in Figure 4, the person conducting the plankton survey will use the above-mentioned reference material (F6) to record the algae disturbance as analysis result A. Also, as shown in "Activated carbon treatment concentration" in Figure 4, the person conducting the plankton survey will use the above-mentioned reference material (F6) to record the algae countermeasures as analysis result A.

[0067] (1-3-2. Problems with the Underwater Particle Countermeasures Support System 100P) The underwater particle countermeasure support system 100P has the following problems. First, in the underwater particle countermeasure support system 100P, plankton investigation takes time and requires a high level of specialized knowledge. Second, in the underwater particle countermeasure support system 100P, it is difficult to quickly reflect the analysis result A from the plankton investigation in the control and operation of the monitoring control device D1.

[0068] (1-3-3. Overview of the Underwater Particle Countermeasures Support System 100-1) An overview of the underwater particle countermeasure support system 100-1 according to embodiment 1 will be described with reference to Fig. 5. Fig. 5 is a diagram showing a specific example of the overview of the underwater particle countermeasure support system 100-1 according to embodiment 1. The sampling device D2, underwater particle image analyzer D3, underwater particle data server D4, underwater particle countermeasure support device D5, display terminal D6, and monitoring control device D1 shown in Fig. 5 will be described below.

[0069] (Sampling device D2) The sampling device D2 collects measurement water W, such as raw water W2 (see "*a"), submerged water W3 (see "*b"), and filtered water W4 (see "*c"), and supplies it to the underwater particle image analysis device D3.

[0070] (Underwater particle image analysis device D3) The underwater particle image analyzer D3 photographs the measurement water W supplied from the underwater particle image analyzer D3 and transmits image data I of the measurement water W to the underwater particle data server D4. The underwater particle image analyzer D3 also measures underwater particles P based on the image library L received from the underwater particle data server D4 and transmits the measurement results M to the underwater particle countermeasures support device D5.

[0071] (Underwater Particle Data Server D4) The underwater particle data server D4 identifies underwater particles P based on the image data I of the measurement water W received from the underwater particle image analyzer D3, and transmits to the underwater particle image analyzer D3 an image library L corresponding to the identified underwater particles P. The underwater particle data server D4 also transmits underwater particle characteristics corresponding to the identified underwater particles P to the underwater particle countermeasures support device D5.

[0072] (Underwater particle countermeasure support device D5) The underwater particle countermeasure support device D5 analyzes underwater particles P based on the measurement results M received from the underwater particle image analysis device D3 and the underwater particle characteristics received from the underwater particle data server D4, and transmits the analysis results A to the display terminal D6.

[0073] (Display terminal D6) The display terminal D6 presents to the operator O countermeasures against underwater particles, such as countermeasures against algae, by displaying on the monitor screen the analysis result A received from the underwater particle countermeasure support device D5.

[0074] (Monitoring control device D1) The monitoring and control device D1 receives underwater particle countermeasures such as countermeasures against algae input by the operator O, and implements underwater particle countermeasures against problems such as coagulation inhibition, filtration blockage, filtration leakage, and generation of unpleasant odors and tastes.

[0075] (1-3-4. Effects of the Underwater Particle Countermeasures Support System 100-1) The underwater particle countermeasure support system 100-1 according to the first embodiment has the following advantages. First, in the underwater particle countermeasure support system 100-1, the water sampling (F1), sample preparation (F2), and species identification and counting (F3) in the plankton survey can be automated by the sampling device D2, the underwater particle image analyzer (D3), and the underwater particle data server (D4), so that the number of steps is significantly reduced and the shortage of personnel with specialized knowledge is eliminated. Second, in the underwater particle countermeasure support system 100-1, the data organization (F4) and judgment (F5) in the plankton survey are converted by the underwater particle countermeasure support device D5 into data for each type of underwater particle P, and various displays are performed, so that underwater particle countermeasures such as algae countermeasures can be quickly implemented.

[0076] 2. Configuration and Processing of Each Device in Underwater Particle Countermeasure Support System 100-1 The configuration and processing of each device of the underwater particle countermeasure support system 100-1 shown in Fig. 1 will be described with reference to Fig. 6. Fig. 6 is a block diagram showing an example of the configuration of each device of the underwater particle countermeasure support system 100-1 according to the first embodiment. In the following, an example of the overall configuration of the underwater particle countermeasure support system 100-1 according to the first embodiment will be described first, and then an example of the configuration and processing of the monitoring control device D1, the sampling device D2, the underwater particle image analysis device D3, the underwater particle data server D4, the underwater particle countermeasure support device D5, and the display terminal D6 according to the first embodiment will be described in detail.

[0077] (2-1. Example of the overall configuration of the underwater particle countermeasure support system 100-1) An example of the overall configuration of the underwater particle countermeasure support system 100-1 shown in FIG. 1 will be described with reference to FIG. 6. As shown in FIG. 6, the underwater particle countermeasure support system 100-1 has a monitoring control device D1, a sampling device D2, an underwater particle image analysis device D3, an underwater particle data server D4, an underwater particle countermeasure support device D5, and a display terminal D6. The monitoring control device D1, the underwater particle image analysis device D3, the underwater particle data server D4, the underwater particle countermeasure support device D5, and the display terminal D6 are communicatively connected by a communication network N realized by the Internet, a dedicated line, or the like. The sampling device D2 and the underwater particle image analysis device D3 are connected by a pipe that supplies the collected measurement water W. The underwater particle data server D4 and the underwater particle countermeasure support device D5 are installed in a cloud environment, but may be installed in an on-premise environment, an edge environment, or the like.

[0078] (2-2. Configuration and Processing Examples of the Monitoring and Control Device D1) A configuration example and a processing example of the monitoring control device D1 will be described with reference to Fig. 6. The monitoring control device D1 is a control device having an execution unit D11 and a communication unit D12. The monitoring control device D1 may also have an input unit (e.g., a keyboard, a mouse, etc.) that receives various operations from an operator O, and a display unit (e.g., a liquid crystal display, etc.) that displays various information.

[0079] (2-2-1. Executive Section D11) The execution unit D11 executes measures against the trouble caused by the underwater particles P. For example, the execution unit D11 executes the powdered activated carbon injection control by sending a control signal to inject powdered activated carbon P9 to the powdered activated carbon injection equipment in response to the powdered activated carbon injection instruction of the operator O. The execution unit D11 also executes the pH adjuster injection control by sending a control signal to inject pH adjuster P10 to the pH adjuster injection equipment in response to the pH adjuster injection instruction of the operator O. The execution unit D11 also executes the flocculant injection control by sending a control signal to inject flocculant P11 to the flocculant injection equipment in response to the flocculant injection instruction of the operator O. The execution unit D11 also executes the filtration basin control of the filtration basin equipment.

[0080] (2-2-2. Communication section D12) The communication unit D12 manages data communication with other devices. For example, the communication unit D12 performs data communication with each communication device via a router, etc. The communication unit D12 can also perform data communication with an operator's terminal (not shown).

[0081] (2-3. Configuration and processing example of sampling device D2) A configuration example and a processing example of the sampling device D2 will be described with reference to Fig. 6. The sampling device D2 is a collection device having a pump D21 and a degassing tank D22. The sampling device D2 collects a plurality of sample waters W at each predetermined measurement position or each predetermined measurement time, and supplies each of the collected sample waters W to the underwater particle image analyzer D3. The sampling device D2 may have an input unit that receives various operations from an operator O and a display unit that displays various information.

[0082] (2-3-1. Pump D21) The pump D21 collects the test water W. For example, the pump D21 collects dam intake water W1 from a water source P1 such as a dam including a water intake facility, collects raw water W2 sent from the water source P1 to a receiving well P2, collects submerged water W3 sent from a settling basin P5 to a rapid sand filter basin P6, collects filtered water W4 sent from the rapid sand filter basin P6 to a purified water reservoir P7, and collects purified water W5 sent from the purified water reservoir P7 to a water distribution facility. The pump D21 also operates continuously at each water inspection point, and always collects new test water W.

[0083] The pump D21 pumps the collected test water W. For example, the pump D21 pumps the collected test water W to a degassing tank D22, and supplies the test water W degassed by the degassing tank D22 to the imaging unit D31 of the underwater particle image analysis device D3.

[0084] (2-3-2. Defoaming tank D22) The defoaming tank D22 defoams the sample water W. For example, the sample water W fed from the pump D21 is released into the defoaming tank D22 to remove dissolved gas.

[0085] (2-4. Configuration and processing example of underwater particle image analysis device D3) A configuration example and a processing example of the underwater particle image analysis device D3 will be described with reference to Fig. 6. The underwater particle image analysis device D3 is an image analysis device having an imaging unit D31, a measurement unit D32, a communication unit D33, and a classification model D34. The underwater particle image analysis device D3 may have an input unit that receives various operations from an operator O and a display unit that displays various information.

[0086] (2-4-1. Shooting section D31) The photographing unit D31 photographs the collected test water W and acquires image data I of the test water W. The photographing unit D31 also transmits the image data I of the test water W to the underwater particle data server D4. The photographing unit D31 includes a cell D31a, a light source D31b, a camera D31c, and a pump D31d.

[0087] (2-4-1-1. Cell D31a) The cell D31a is a transparent container that contains the test water W that is supplied from the sampling device D2 and is to be photographed.

[0088] (2-4-1-2.Light source D31b) The light source D31b is a light source device that is contained in the cell D31a and irradiates light onto the test water W to be photographed.

[0089] (2-4-1-3. Camera D31c) The camera D31c is an image capturing device and captures an image of the test water W contained in the cell D31a.

[0090] (2-4-1-4. Pump D31d) The pump D31d draws out a certain amount of the measured water W to be photographed. The pump D31d also returns the photographed measured water W to the sampling device D2 and drains it.

[0091] (2-4-2.Measuring part D32) The measurement unit D32 can be realized by, for example, electronic circuits such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit) or integrated circuits such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The measurement unit D32 measures underwater particles P contained in the measurement water W based on image data I of the measurement water W. Below, the classification model learning process, underwater particle classification process, feature amount calculation process, and particle number measurement process will be described.

[0092] (2-4-2-1. Classification model learning process) The measurement unit D32 executes a classification model learning process. For example, the measurement unit D32 acquires an image library L including image data of the underwater particles P identified by the underwater particle data server D4 from the image data I, inputs the image data as learning data to the classification model D34, and executes machine learning or statistical processing so that the classification model D34 outputs the type of the underwater particle P, thereby constructing a learned classification model D34.

[0093] (2-4-2-2. Underwater particle classification processing) The measurement unit D32 executes an underwater particle classification process. For example, the measurement unit D32 classifies the underwater particles P included in the image data I of the measurement water W using an image library L including image data of the underwater particles P.

[0094] At this time, the measurement unit D32 uses the trained classification model D34 to classify the underwater particles P contained in the image data I of the measurement water W. To explain this by taking a specific example, the measurement unit D32 acquires, as a classification result, the types of the underwater particles P, such as various algae, pollen, silt, and microplastics, output by the trained classification model D34 in response to the input of the image data I of the measurement water W.

[0095] Furthermore, the measurement unit D32 uses the trained classification model D34 to perform statistical analysis on the image library L including the image data of the identified underwater particles P, and classifies the underwater particles P in the image data I using the extracted feature amounts of the underwater particles P. To explain this by taking a specific example, the measurement unit D32 uses the trained classification model D34 to perform statistical analysis on the image library L, extracts the feature amounts of the underwater particles P, calculates the feature amounts of the underwater particles P included in the image data I of the measurement water W by a feature amount calculation process described later, and obtains the types of the underwater particles P, such as various algae, pollen, silt, and microplastics, as classification results according to the similarity of the feature amounts.

[0096] (2-4-2-3. Feature Calculation Processing) The measurement unit D32 executes a feature calculation process. For example, the measurement unit D32 calculates the feature of the underwater particle P by analyzing the image data I of the test water W. At this time, the measurement unit D32 calculates the particle diameter, length and width, aspect ratio, chromaticity, etc. as the feature of the underwater particle P by analyzing the image data I of the test water W.

[0097] (2-4-2-4. Particle number measurement processing) The measurement unit D32 executes a particle number measurement process. For example, the measurement unit D32 measures the number of underwater particles P using the classification results of the underwater particles P or the calculation results of the feature amounts of the underwater particles P.

[0098] At this time, the measurement unit D32 measures the number of particles for each classified underwater particle P. To explain this by taking a specific example, the measurement unit D32 measures the number of particles for each type of underwater particle P, such as various algae, pollen, silt, microplastics, etc., classified by the above-mentioned underwater particle classification process, and outputs the number of particles per 1 mL of measurement water W (cell / mL).

[0099] Furthermore, the measurement unit D32 measures the number of underwater particles P having similar calculated feature amounts. To explain this by taking a specific example, the measurement unit D32 measures the number of particles for each predetermined range of the feature amounts of the underwater particles P, such as particle diameter, length and width, aspect ratio, and chromaticity, calculated by the above-mentioned feature amount calculation process, and outputs the number of particles for each particle diameter, the number of particles for each length and width, the number of particles for each aspect ratio, the number of particles for each chromaticity, and the like, as a particle size distribution.

[0100] (2-4-3. Communication section D33) The communication unit D33 manages data communication with other devices. For example, the communication unit D33 performs data communication with each communication device via a router, etc. The communication unit D33 can also perform data communication with an operator's terminal (not shown).

[0101] (2-4-4. Classification model D34) The classification model D34 is model data of a machine learning model or a statistical model, and is data including, for example, execution data for executing an algorithm for classifying underwater particles P, model parameters which are setting values, hyperparameters, and the like.

[0102] (2-5. Example of the configuration and processing of the underwater particle data server D4) A configuration example and a processing example of the underwater particle data server D4 will be described with reference to Fig. 6. The underwater particle data server D4 is a server device having an analysis unit D41, an underwater particle database D42, and a communication unit D43. The underwater particle data server D4 may have an input unit that receives various operations from an operator O and a display unit that displays various information.

[0103] (2-5-1.Analysis Department D41) The analysis unit D41 can be realized by, for example, an electronic circuit such as a CPU or an MPU, or an integrated circuit such as an ASIC or an FPGA. The analysis unit D41 identifies underwater particles P contained in the measurement water W based on image data I of the measurement water W transmitted from the underwater particle image analysis device D3, transmits an image library L including image data of the identified underwater particles P to the underwater particle image analysis device D3, and transmits characteristics of the identified underwater particles P to the underwater particle countermeasures support device D5.

[0104] To explain this with a specific example, first, the analysis unit D41 calculates the feature amount of the underwater particles P included in the image data I of the measurement water W, compares it with the feature amount of the underwater particles P stored in the underwater particle database D42, and identifies the type of the underwater particles P, such as various algae, pollen, silt, microplastics, etc., according to the similarity of the feature amount. Second, the analysis unit D41 refers to the image library L of the underwater particle database D42, acquires the image library L corresponding to the identified type of underwater particle P, and transmits it to the underwater particle image analysis device D3. Third, the analysis unit D41 refers to the underwater particle data D of the underwater particle database D42, acquires the underwater particle characteristics corresponding to the identified type of underwater particle P, and transmits it to the underwater particle countermeasure support device D5.

[0105] (2-5-2. Underwater particle database D42) The underwater particle database D42 stores various information referred to when the analysis unit D41 operates and various information acquired when the analysis unit D41 operates. The underwater particle database D42 stores underwater particle data D and an image library L. Here, the underwater particle database D42 can be realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. In the example of Fig. 6, the underwater particle database D42 is installed inside the underwater particle data server D4, but it may be installed outside the underwater particle data server D4, or a plurality of underwater particle databases D42 may be installed.

[0106] (2-5-2-1. Underwater particle data D) The underwater particle data D is various information related to the underwater particles P. An example of the underwater particle data D will now be described with reference to Fig. 7. Fig. 7 is a diagram showing an example of the underwater particle data D in the underwater particle data server according to the first embodiment. In the example of Fig. 7, the underwater particle data D has items such as "classification", "name", "obstacle classification", "underwater particle characteristics", and "image library".

[0107] "Classification" indicates the type of underwater particle P, for example, a class in the classification of algae. "Name" indicates the type of underwater particle P, for example, a species in the classification of algae. "Problem classification" indicates the type of problem caused by the underwater particle P, for example, types of algae problems such as coagulation inhibition, filtration blockage, filtration leakage, and generation of unpleasant odors and tastes. "Underwater particle characteristics" indicates properties corresponding to the species of the underwater particle P, for example, causative substances that cause problems in water purification treatment generated by the underwater particle P, the concentration of causative substances per particle of the underwater particle P, and the specific gravity of the underwater particle P. "Image library" is an image library L described later, and is, for example, multiple image data of underwater particles P corresponding to the species of the underwater particle P, indicating the shape, color, and size of the underwater particle P.

[0108] That is, in Figure 7, examples of individual underwater particle data D for underwater particle P {classification: "Cyanobacteria", name: "Anabaena Spiroides"} are shown, such as {problem classification: "unpleasant odor / taste (moldy odor)", underwater particle characteristics (causing substance): "geosmin", underwater particle characteristics (concentration): "-", underwater particle characteristics (specific gravity): "-"}.

[0109] (2-5-2-2. Image Library L) The image library L is image data showing the shape, color, and size of underwater particles P. An example of the data in the image library L will now be described with reference to Fig. 8. Fig. 8 is a diagram showing an example of the image library L of the underwater particle data server according to the first embodiment. Fig. 8 shows an example of the image library L including a plurality of image data for a single identified algae species.

[0110] (2-5-3. Communication section D43) The communication unit D43 manages data communication with other devices. For example, the communication unit D43 performs data communication with each communication device via a router, etc. The communication unit D43 can also perform data communication with an operator's terminal (not shown).

[0111] (2-6. Configuration and processing example of underwater particle countermeasure support device D5) An example of the configuration and processing of the underwater particle countermeasure support device D5 will be described with reference to Fig. 6 again. The underwater particle countermeasure support device D5 is a server device having an analysis unit D51, an analysis result database D52, and a communication unit D53. The underwater particle countermeasure support device D5 may also have an input unit that accepts various operations from an operator O and a display unit that displays various information.

[0112] (2-6-1. Analysis Department D51) Here, the analysis unit D51 can be realized by, for example, electronic circuits such as a CPU or MPU, or integrated circuits such as an ASIC or FPGA. Based on the measurement results of the underwater particles P transmitted from the underwater particle image analyzer D3, the analysis unit D51 outputs analysis results used for measures against troubles caused by the underwater particles P. Below, the underwater particle countermeasures report output process, the measurement location graph output process, and the time series trend graph output process will be described.

[0113] (2-6-1-1. Underwater particle countermeasures report output processing) The analysis unit D51 executes an underwater particle countermeasure form output process. For example, the analysis unit D51 outputs an underwater particle countermeasure form A1 which is an analysis result A including at least one of the measurement result M transmitted from the underwater particle image analyzer D3, the characteristics of the underwater particles P identified using the image data I of the test water W, and the type of obstacle based on the characteristics of the underwater particles P.

[0114] To explain the underwater particle countermeasures report output process using a specific example, the analysis unit D51 outputs an underwater particle countermeasures report A1 including the measurement results M of the number of underwater particles P per mL contained in the dam intake water W1, raw water W2, submerged water W3, filtered water W4, and purified water W5 obtained from the underwater particle image analysis device D3, and stores the result in an analysis result database D52.

[0115] The analysis unit D51 also outputs an underwater particle countermeasures form A1 including underwater particle characteristics for each underwater particle P, which indicate the causative substances that cause impediments in water purification treatment generated by the underwater particles P, the concentration of the causative substances per particle of the underwater particles P, the specific gravity of the underwater particles P, etc., obtained from the underwater particle data server D4, and stores the form A1 in the analysis result database D52.

[0116] Furthermore, the analysis unit D51 uses the underwater particle characteristics acquired from the underwater particle data server D4 to identify the type of trouble, such as coagulation inhibition, filtration blockage, filtration leakage, or generation of unpleasant odors and tastes, corresponding to the underwater particle characteristics, outputs an underwater particle countermeasure form A1 including the type of trouble for each underwater particle P, and stores it in the analysis result database D52. At this time, the analysis unit D51 may identify the type of trouble by referring to the underwater particle data D held by the underwater particle data server D4, or may identify the type of trouble by referring to the underwater particle data D held by the underwater particle countermeasure support device D5.

[0117] (2-6-1-2. Measurement location graph output processing) The analysis unit D51 executes a measurement location graph output process. For example, the analysis unit D51 outputs a measurement location graph A2, which is an analysis result A including at least one of a graph showing the measurement results transmitted from the underwater particle image analyzer D3 and a graph showing the degree of occurrence of a fault based on the characteristics of the underwater particles P, for each of a plurality of measurement waters W collected at a plurality of measurement locations.

[0118] To explain the measurement location graph output process using a specific example, the analysis unit D51 outputs a measurement location graph A2 showing the number of particles per mL of measurement water W for each underwater particle P as a line graph for the water testing points of the dam intake water W1, raw water W2, submerged water W3, filtered water W4, and purified water W5, and stores the graph in the analysis result database D52.

[0119] In addition, the analysis unit D51 totals the number of underwater particles P corresponding to each type of obstruction, such as unpleasant odors and tastes, filter blockages, and filter leaks, for the water testing points of the dam intake water W1, raw water W2, submerged water W3, filtered water W4, and purified water W5, outputs a measurement point graph A2 showing the total number of particles per 1 mL of measured water W for each type of obstruction as a line graph, and stores the total number of particles in the analysis result database D52.

[0120] (2-6-1-3. Time series trend graph output processing) The analysis unit D51 executes a time series trend graph output process. For example, the analysis unit D51 outputs a time series trend graph A3, which is an analysis result A including at least one of a graph showing the measurement results transmitted from the underwater particle image analyzer D3 and a graph showing the degree of occurrence of a fault based on the characteristics of the underwater particles P, for each of the multiple test waters W collected at each of the multiple measurement times.

[0121] To explain the time series trend graph output process using a specific example, the analysis unit D51 outputs a time series trend graph A3 showing the number of particles per 1 mL of measurement water W for each underwater particle P in a line graph for five alternate days, and stores the graph in the analysis result database D52.

[0122] In addition, the analysis unit D51 totals the number of particles P in the water corresponding to each type of problem, such as unpleasant odors and tastes, filtration blockages, and filtration leaks, for every other day over five days, outputs a time-series trend graph A3 showing the total number of particles per mL of measurement water W for each type of problem as a line graph, and stores the total number of particles in the analysis result database D52.

[0123] (2-6-2. Analysis results database D52) The analysis result database D52 stores various information referred to when the analysis unit D51 operates and various information acquired when the analysis unit D51 operates. The analysis result database D52 stores the underwater particle countermeasure form A1, the measurement location graph A2, and the time series trend graph A3. Here, the analysis result database D52 can be realized by, for example, a semiconductor memory element such as a RAM or a flash memory, or a storage device such as a hard disk or an optical disk. In the example of FIG. 6, the analysis result database D52 is installed inside the underwater particle countermeasure support device D5, but it may be installed outside the underwater particle countermeasure support device D5, or multiple analysis result databases D52 may be installed. In addition, the analysis result database D52 may store underwater particle data D.

[0124] (2-6-2-1. Underwater particle countermeasures form A1) The underwater particle countermeasure form A1 is the analysis result A output by the analysis unit D51. An example of data of the underwater particle countermeasure form A1 will now be described with reference to FIG. 9. FIG. 9 is a diagram showing an example of the underwater particle countermeasure form A1 of the underwater particle countermeasure support device D5 according to the first embodiment. In the example of FIG. 9, the underwater particle countermeasure form A1 has items such as "Classification", "Name", "Measurement Results", "Fault Classification", "Underwater Particle Characteristics", and "Image Library". In addition, the underwater particle countermeasure form A1 has an item such as "Fault-by-Fault Aggregation".

[0125] "Classification" indicates the type of underwater particle P, for example, a class in the classification of algae. "Name" indicates the type of underwater particle P, for example, a species in the classification of algae. "Measurement result" indicates the number of particles of each underwater particle P contained in the measurement water W, for example, the number of particles (cell / mL) of each underwater particle P per 1 mL of measurement water W measured at each water inspection point of the dam intake water W1, the raw water W2, the submerged water W3, the filtered water W4, and the purified water W5. "Problem classification" indicates the type of trouble caused by the underwater particle P, for example, the type of algae trouble such as coagulation inhibition, filtration blockage, filtration leakage, and generation of unpleasant odors and tastes. "Underwater particle characteristics" indicates properties corresponding to the type of underwater particle P, for example, the causative substance of trouble in water purification treatment generated by the underwater particle P, the concentration of the causative substance per particle of the underwater particle P, the specific gravity of the underwater particle P, etc. The "image library" is the image library L described above, and is, for example, a plurality of image data I of underwater particles P corresponding to the types of underwater particles P, indicating the shape, color, and size of the underwater particles P. The "tally by type of trouble" indicates the occurrence rate of each type of trouble caused by the underwater particles P, and is, for example, the total number of particles (cell / mL) per 1 mL of measured water W calculated from the underwater particles P for each type of trouble, such as unpleasant odors and tastes, filtration blockages, filtration leaks, etc.

[0126] That is, in Figure 9, examples of individual underwater particle countermeasures form A1 for underwater particle P {classification: "Cyanobacteria", name: "Anabaena spiroides"} are shown, such as {measurement result (dam intake water): "500", measurement result (raw water): "450", measurement result (submerged water): "80", measurement result (filtered water): "0.1", measurement result (purified water): "0", problem classification: "unpleasant taste / odor (moldy smell)", underwater particle characteristics (causing substance): "geosmin", underwater particle characteristics (concentration): "0.001", underwater particle characteristics (specific gravity): "1.04"}. In addition, examples of "Torture by type of trouble" and "Taste and odor" in the underwater particle countermeasures form A1 by type of trouble include {Measurement result (dam intake water): "1,750", Measurement result (raw water): "1,490", Measurement result (submerged water): "310", Measurement result (filtered water): "0.3", Measurement result (purified water): "0"}.

[0127] (2-6-2-2. Measurement location graph A2) The measurement location graph A2 is the analysis result A output by the analysis unit D51. An example of data of the measurement location graph A2 will now be described with reference to Fig. 10. Fig. 10 is a diagram showing an example of the measurement location graph A2 of the underwater particle countermeasure support device D5 according to the first embodiment. In the example of Fig. 10, the measurement location graph A2 shows graphs such as "underwater particles / measurement location" and "tally by fault / measurement location".

[0128] An example of "underwater particles / measurement location" is measurement location graph A2, which shows a line graph of the number of particles (cell / mL) per 1 mL of measured water W for each of the underwater particles P of "Cyanobacteria Anabaena spiroides," "Cyanobacteria Oscillatoria agardhii," "Cyanobacteria Oscillatoria cortiana," "Cyanobacteria Synechococcus sp," "Bacillariophyceae Synedra acus," and "silt" for the water test points of dam intake water W1, raw water W2, submerged water W3, filtered water W4, and purified water W5.

[0129] An example of "Count by type of trouble / measurement location" is measurement location graph A2, which shows a line graph of the total number of particles (cell / mL) per 1 mL of measured water W for each type of trouble, namely "unpleasant taste / odor," "filter blockage," "filter leakage," and "other," for the water testing points of dam intake water W1, raw water W2, submerged water W3, filtered water W4, and purified water W5.

[0130] (2-6-2-3. Time series trend graph A3) The time series trend graph A3 is the analysis result A output by the analysis unit D51. An example of data of the time series trend graph A3 will now be described with reference to Fig. 11. Fig. 11 is a diagram showing an example of the time series trend graph A3 of the underwater particle countermeasure support device D5 according to the first embodiment. In the example of Fig. 11, the time series trend graph A3 shows graphs such as "underwater particle trend" and "tally trend by fault".

[0131] An example of the "underwater particle trend" is time-series trend graph A3, which shows a line graph of the number of particles (cell / mL) per 1 mL of measured water W for each of the underwater particles P of "Anabaena spiroides", "Oscillatoria agardhii", "Oscillatoria cortiana", "Synechococcus sp", "Synechococcus acus", and "Silt" for every other five days on "August 1", "August 2", "August 3", "August 4", and "August 5".

[0132] An example of the “aggregated trend by fault” is time-series trend graph A3, which shows a line graph of the total number of particles (cell / mL) per 1 mL of measured water W for each type of fault, namely, “unpleasant taste / odor,” “filter blockage,” “filter leakage,” and “other,” for the alternating five-day period of “August 1,” “August 2,” “August 3,” “August 4,” and “August 5.”

[0133] (2-6-3. Communication section D53) The communication unit D53 manages data communication with other devices. For example, the communication unit D53 performs data communication with each communication device via a router, etc. The communication unit D53 can also perform data communication with an operator's terminal (not shown).

[0134] (2-7. Example of the configuration and processing of display terminal D6) A configuration example and a processing example of the display terminal D6 will be described with reference to Fig. 6 again. The display terminal D6 is a terminal used by an operator O who is a manager who manages the sample water W, and is a manager terminal having an input / output unit D61, a control unit D62, and a communication unit D63.

[0135] (2-7-1. Input / output section D61) The input / output unit D61 controls input of various information to the display terminal D6. For example, the input / output unit D61 is realized by a mouse, a keyboard, a touch panel, etc., and accepts input of setting information, etc. to the display terminal D6. The input / output unit D61 also controls display of various information from the display terminal D6. For example, the input / output unit D61 is realized by a display, etc., and displays setting information, etc. stored in the display terminal D6.

[0136] Furthermore, the input / output unit D61 displays the analysis results A transmitted from the underwater particle countermeasures support device D5. For example, the input / output unit D61 displays, as the analysis results A, an underwater particle countermeasures form A1, a measurement location graph A2, a time series trend graph A3, etc.

[0137] (2-7-2. Control unit D62) The control unit D62 transmits various information. The control unit D62 also receives various information. For example, the control unit D62 receives the analysis result A from the underwater particle countermeasure support device D5.

[0138] (2-7-3. Communication section D63) The communication unit D63 manages data communication with other devices. For example, the communication unit D63 performs data communication with each communication device via a router, etc. The communication unit D63 can also perform data communication with an operator's terminal (not shown).

[0139] 3. Processing flow of the underwater particle countermeasure support system 100-1 The process flow of the underwater particle countermeasure support system 100-1 according to the first embodiment will be described with reference to Fig. 12. Fig. 12 is a flowchart showing an example of the process flow of the underwater particle countermeasure support system 100-1 according to the first embodiment. Note that the processes of steps S101 to S113 below can also be executed in a different order. Also, some of the processes of steps S101 to S113 below may be omitted.

[0140] (3-1. Measurement water collection and processing) First, the sampling device D2 executes a measurement water collection process (step S101). For example, the sampling device D2 collects dam intake water W1, raw water W2, submerged water W3, filtered water W4, and purified water W5.

[0141] (3-2. Measurement water photography processing) Secondly, the underwater particle image analysis device D3 executes a measurement water photographing process (step S102). For example, the underwater particle image analysis device D3 photographs the measurement water W supplied from the sampling device D2 through a pipe, and obtains image data I of the sampled measurement water W.

[0142] (3-3. Image data transmission process) Thirdly, the underwater particle image analysis device D3 executes an image data transmission process (step S103). For example, the underwater particle image analysis device D3 transmits the acquired image data I of the measurement water W to the underwater particle data server D4.

[0143] (3-4. Underwater particle identification processing) Fourth, the underwater particle data server D4 executes an underwater particle identification process (step S104). For example, the underwater particle data server D4 executes image analysis of the image data I of the measurement water W transmitted from the underwater particle image analyzer D3, and identifies the types of the underwater particles P contained in the measurement water W.

[0144] (3-5. Image library transmission process) Fifth, the underwater particle data server D4 executes an image library transmission process (step S105). For example, the underwater particle data server D4 transmits the image library L corresponding to the identified underwater particle P to the underwater particle image analysis device D3.

[0145] (3-6. Underwater particle characteristics transmission processing) Sixth, the underwater particle data server D4 executes an underwater particle characteristic transmission process (step S106). For example, the underwater particle data server D4 transmits the image library L corresponding to the identified underwater particle P to the underwater particle image analysis device D3.

[0146] (3-7. Underwater particle measurement processing) Seventh, the underwater particle image analyzer D3 executes an underwater particle measurement process (step S107). For example, the underwater particle image analyzer D3 classifies the underwater particles P in the image data I by executing machine learning or statistical analysis using the image library L transmitted from the underwater particle data server D4, and measures the number of particles for each type of the classified underwater particles P.

[0147] (3-8. Measurement result transmission process) Eighth, the underwater particle image analysis device D3 executes a measurement result transmission process (step S108). For example, the underwater particle image analysis device D3 transmits the particle count for each type of classified underwater particles P as the measurement result M to the underwater particle countermeasure support device D5.

[0148] (3-9. Underwater particle analysis processing) Ninth, the underwater particle countermeasure support device D5 executes an underwater particle analysis process (step S109). For example, the underwater particle countermeasure support device D5 outputs, as the analysis result A, an underwater particle countermeasure form A1 including the measurement result M, underwater particle characteristics, and fault classification, a measurement point graph A2 including a graph showing the measurement result M for each water inspection point of the test water W and the occurrence degree of the fault, and a time-series trend graph A3 including a graph showing the measurement result M for each measurement date of the test water W and the occurrence degree of the fault.

[0149] (3-10. Analysis result transmission process) Tenth, the underwater particle countermeasure support device D5 executes an analysis result transmission process (step S110). For example, the underwater particle countermeasure support device D5 transmits an underwater particle countermeasure form A1, a measurement location graph A2, a time series trend graph A3, and the like as the analysis result A to the display terminal D6.

[0150] (3-11. Analysis result display processing) Eleventh, the display terminal D6 executes an analysis result display process (step S111). For example, the display terminal D6 displays, as the analysis result A, an underwater particle countermeasure form A1, a measurement location graph A2, a time series trend graph A3, and the like on the monitor screen.

[0151] (3-12. Input processing for underwater particle countermeasures) Twelfthly, the operator O executes an underwater particle countermeasure input process (step S112). For example, the operator O inputs underwater particle countermeasures such as powdered activated carbon injection, pH adjuster injection, and coagulant injection to the monitoring and controlling device D1 based on the analysis result A displayed on the monitor screen of the display terminal D6.

[0152] (3-13. Underwater particle countermeasure execution processing) Thirteenthly, the monitoring and controlling device D1 executes the underwater particle countermeasure execution process (step S113) and ends the process. For example, the monitoring and controlling device D1 executes the underwater particle countermeasure of the water purification facility by optimally executing the powdered activated carbon injection control of the powdered activated carbon injection facility, the pH adjuster injection control of the pH adjuster injection facility, the coagulant injection control of the coagulant injection facility, and the filtration facility control of the filtration facility.

[0153] 4. Effects of the First Embodiment Finally, a description will be given of the effects of the first embodiment. Below, effects 1 to 11 corresponding to the processing according to the first embodiment will be described.

[0154] (4-1. Effect 1) First, in the process according to the above-mentioned embodiment 1, the underwater particle image analysis device D3 photographs the collected test water W, acquires image data I of the test water W, measures the underwater particles P contained in the test water W based on the acquired image data I of the test water W, and the underwater particle countermeasure support device D5 outputs an analysis result A used for countermeasures against damage caused by the underwater particles P based on the measurement result M of the underwater particles P transmitted from the underwater particle image analysis device D3. Therefore, in this process, countermeasures against damage caused by the underwater particles P can be efficiently implemented.

[0155] (4-2. Effect 2) Secondly, in the process according to the above-mentioned embodiment 1, the underwater particle data server D4 identifies underwater particles P contained in the measurement water W based on image data I of the measurement water W transmitted from the underwater particle image analyzer D3, transmits an image library L including image data of the identified underwater particles P to the underwater particle image analyzer D3, and transmits characteristics of the identified underwater particles P to the underwater particle countermeasures support device D5. Therefore, in this process, by utilizing the data held by the underwater particle data server D4, countermeasures against problems caused by the underwater particles P can be efficiently implemented.

[0156] (4-3. Effect 3) Thirdly, in the process according to the above-mentioned embodiment 1, the sampling device D2 collects a plurality of samples of test water W and supplies each of the collected samples of test water W to the underwater particle image analyzer D3. Therefore, in this process, by using the sampling device D2 that automatically collects the test water W, measures against problems caused by underwater particles P can be efficiently implemented.

[0157] (4-4. Effect 4) Fourthly, in the process according to the above-mentioned embodiment 1, the underwater particle image analysis device D3 classifies the underwater particles P contained in the image data I of the measurement water W using an image library L including image data of the underwater particles P, and measures the number of particles for each classified underwater particle P. Therefore, in this process, by automatically classifying the underwater particles P contained in the measurement water W and outputting the measurement results M for each underwater particle P, it is possible to efficiently implement measures against problems caused by the underwater particles P.

[0158] (4-5. Effect 5) Fifth, in the process according to the above-mentioned embodiment 1, the underwater particle image analyzer D3 calculates the feature quantities of the underwater particles P by analyzing the image data I of the test water W, and measures the particle count for each underwater particle P having a similar calculated feature quantity. Therefore, in this process, by automatically outputting the measurement result M including the particle size distribution of the test water W, it is possible to efficiently implement measures against problems caused by the underwater particles P.

[0159] (4-6. Effect 6) Sixth, in the process according to the above-mentioned embodiment 1, the underwater particle countermeasure support device D5 outputs an underwater particle countermeasure form A1 including at least one of the measurement results M, the characteristics of the underwater particles P identified using the image data I of the test water W, and the type of obstacle based on the characteristics of the underwater particles P. Therefore, in this process, by automatically outputting the analysis results A including the type of obstacle together with the measurement results M of the test water W, it is possible to efficiently implement countermeasures against obstacles caused by the underwater particles P.

[0160] (4-7. Effect 7) Seventh, in the process according to the above-mentioned embodiment 1, the underwater particle countermeasure support device D5 outputs, for each of the multiple pieces of test water W collected at each of the multiple measurement positions, a measurement position graph A2 including at least one of a graph showing the measurement result M and a graph showing the degree of occurrence of a fault based on the characteristics of the underwater particles P. Therefore, in this process, by automatically outputting the analysis result A including a fault-specific tally for each measurement position, it is possible to efficiently implement countermeasures against faults caused by the underwater particles P.

[0161] (4-8. Effect 8) Eighth, in the process according to the above-described embodiment 1, the underwater particle countermeasure support device D5 outputs, for each of the multiple sample waters W collected at each of the multiple measurement times, a time-series trend graph A3 including at least one of a graph showing the measurement results M and a graph showing the degree of occurrence of a fault based on the characteristics of the underwater particles P. Therefore, in this process, by automatically outputting the analysis results A including a fault-specific tally for each measurement time, it is possible to efficiently implement countermeasures against faults caused by the underwater particles P.

[0162] (4-9. Effect 9) Ninth, in the process according to the above-mentioned embodiment 1, the display terminal D6 is a terminal used by the operator O who manages the sample water W, and displays the analysis result A transmitted from the underwater particle countermeasure support device D5. Therefore, in this process, by using the display terminal D6 that automatically provides the analysis result A of the underwater particles P contained in the sample water W to the operator O, countermeasures against problems caused by the underwater particles P can be efficiently implemented.

[0163] (4-10. Effect 10) Tenth, in the process according to the above-mentioned embodiment 1, the test water W is collected at each stage of the purification process of the water supply. Therefore, in this process, it is possible to efficiently implement measures against problems caused by the underwater particles P at each stage of the purification process of the water supply.

[0164] (4-11. Effect 11) Eleventh, in the process according to the above-described first embodiment, the underwater particles P are algae contained in the measurement water W. Therefore, in this process, it is possible to efficiently implement measures against algae damage caused by algae among the underwater particles P.

[0165] [5. System] The information including the processing procedures, control procedures, specific names, various data and parameters shown in the above documents and drawings can be changed arbitrarily unless otherwise specified.

[0166] In addition, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure. In other words, all or part of them can be functionally or physically distributed and integrated in any unit according to various loads, usage conditions, etc.

[0167] Furthermore, each processing function performed by each device may be realized, in whole or in part, by a CPU and a program analyzed and executed by the CPU, or may be realized as hardware using wired logic.

[0168] [6. Hardware] Next, an example of the hardware configuration of an underwater particle image analysis device D3, which is an image analysis device, will be described. Note that other devices may have a similar hardware configuration. FIG. 13 is a diagram showing an example of the hardware configuration according to the first embodiment. As shown in FIG. 13, the underwater particle image analysis device D3 has a communication device D3a, a HDD (Hard Disk Drive) D3b, a memory D3c, and a processor D3d. Furthermore, each unit shown in FIG. 13 is connected to each other via a bus or the like.

[0169] The communication device D3a is a network interface card or the like, and communicates with other servers. The HDD D3b stores programs and databases that operate the functions shown in FIG.

[0170] The processor D3d reads out a program that executes the same processes as the processing units shown in FIG. 6 from the HDD D3b etc. and loads it in the memory D3c, thereby operating a process that executes the functions described in FIG. 6 etc. For example, this process executes the same functions as the processing units of the underwater particle image analysis device D3. Specifically, the processor D3d reads out a program having the same functions as the photographing unit D31, the measuring unit D32 etc. from the HDD D3b etc. Then, the processor D3d executes a process that executes the same processes as the photographing unit D31, the measuring unit D32 etc.

[0171] In this way, the underwater particle image analysis device D3 operates as a device that executes various processing methods by reading and executing a program. The underwater particle image analysis device D3 can also realize functions similar to those of the above-mentioned embodiment 1 by reading the program from a recording medium using a medium reading device and executing the read program. Note that the program according to embodiment 1 is not limited to being executed by the underwater particle image analysis device D3. For example, the present invention can be similarly applied to cases where another computer or server executes a program, or where these execute a program in cooperation with each other.

[0172] This program can be distributed via a network such as the Internet. This program can also be recorded on a computer-readable recording medium such as a hard disk, a flexible disk (FD), a CD-ROM, a magneto-optical disk (MO), or a digital versatile disk (DVD), and can be executed by being read out from the recording medium by a computer.

[0173] [Embodiment 2] Below, the configuration and processing of underwater particle countermeasure support system 100-2 according to embodiment 2, the configuration and processing of each device of underwater particle countermeasure support system 100-2 will be described in order, and finally, the effects of embodiment 2 will be described. Note that descriptions of configurations and processing common to underwater particle countermeasure support system 100-1 according to embodiment 1 will be omitted.

[0174] 1. Configuration and Processing of Underwater Particle Countermeasure Support System 100-2 The configuration and processing of underwater particle countermeasure support system 100-2 according to embodiment 2 will be described in detail with reference to Fig. 14. Fig. 14 is a diagram showing an example of the configuration and processing of underwater particle countermeasure support system 100-2 according to embodiment 2. An example of the overall processing of underwater particle countermeasure support system 100-2 will be described below. Note that the example of the overall configuration of underwater particle countermeasure support system 100-2 is common to the example of the overall configuration of underwater particle countermeasure support system 100-1, so description thereof will be omitted.

[0175] (1-1. Example of overall processing of underwater particle countermeasure support system 100-2) An example of the overall processing of the underwater particle countermeasure support system 100-2 will be described with reference to Fig. 14. Note that the processes in Fig. 14(21)-(25) below can be executed in a different order. Also, some of the processes in Fig. 14(21)-(25) below may be omitted. Also, the process prior to the process in Fig. 14(21) below is the same as the processes in Fig. 1(1)-(8) described in the first embodiment, so the description thereof will be omitted.

[0176] (1-1-1. Taste and odor underwater particle analysis processing) First, the underwater particle countermeasure support device D5 analyzes underwater particles P related to unpleasant odors and tastes (see FIG. 14 (21)). For example, the underwater particle countermeasure support device D5 calculates an assumed odorant concentration using the measurement results M of the underwater particles P transmitted from the underwater particle image analyzer D3, and outputs the calculated assumed odorant concentration as the analysis result A. At this time, the underwater particle countermeasure support device D5 may output, as the analysis result A, an assumed odorant concentration graph A4 including a measurement point graph showing the assumed odorant concentration for each test point of the test water W, and a time-series trend graph showing the assumed odorant concentration for each measurement date of the test water W.

[0177] In addition, the underwater particle countermeasure support device D5 calculates the injection rate of powdered activated carbon P9 using the measurement results M of the underwater particles P transmitted from the underwater particle image analysis device D3, and outputs the calculated injection rate of powdered activated carbon as the analysis result A.

[0178] (1-1-2. Taste and odor analysis result transmission processing) Secondly, the underwater particle countermeasure support device D5 transmits the analysis result A regarding the unpleasant odor and taste to the display terminal D6 (see FIG. 14 (22)). For example, the underwater particle countermeasure support device D5 transmits the expected odorant concentration, the expected odorant concentration graph A4, the powdered activated carbon injection rate, etc. as the analysis result A to the display terminal D6.

[0179] (1-1-3. Display processing of taste and odor analysis results) Third, the display terminal D6 displays the analysis result A regarding the taste or odor (see FIG. 14(23)). For example, the display terminal D6 displays the analysis result A on the monitor screen, such as the assumed odorant concentration, the assumed odorant concentration graph A4, and the powdered activated carbon injection rate.

[0180] (1-1-4. Input processing for measures against unpleasant odors and tastes in water particles) Fourth, the operator O inputs the underwater particle countermeasures for the unpleasant taste and odor to the monitoring and control device D1 (see FIG. 14 (24)). For example, the operator O inputs an instruction to execute the underwater particle countermeasure of injecting powdered activated carbon to the monitoring and control device D1 based on the analysis result A displayed on the monitor screen of the display terminal D6.

[0181] (1-1-5. Processing to deal with odorous and tasteful underwater particles) Fifth, the monitoring and controlling device D1 implements measures against tasteful and odorous underwater particles (see FIG. 14 (25)). For example, the monitoring and controlling device D1 implements measures against tasteful and odorous underwater particles among the measures against underwater particles in the water purification facility by controlling the powdered activated carbon injection of the powdered activated carbon injection facility according to the calculated powdered activated carbon injection rate.

[0182] 2. Configuration and Processing of Each Device in Underwater Particle Countermeasure Support System 100-2 Referring again to FIG. 6, the configuration and processing of each device of the underwater particle countermeasure support system 100-2 shown in FIG. 14 will be described. Note that the configuration example of each device of the underwater particle countermeasure support system 100-2 according to the second embodiment is common to the configuration example of each device of the underwater particle countermeasure support system 100-1 according to the first embodiment shown in FIG. 6. Below, the configuration example and processing example of the monitoring control device D1, the configuration example and processing example of the underwater particle countermeasure support device D5, and the configuration example and processing example of the display terminal D6 according to the second embodiment will be described in detail. Note that the configuration example of the entire underwater particle countermeasure support system 100-2 according to the second embodiment, the configuration example and processing example of the sampling device D2, the configuration example and processing example of the underwater particle image analysis device D3, and the configuration example and processing example of the underwater particle data server D4 are common to the first embodiment, so the description will be omitted.

[0183] (2-1. Configuration and Processing Examples of the Monitoring and Control Device D1) A configuration example and a processing example of the monitoring control device D1 will be described. The monitoring control device D1 is a control device having an execution unit D11 and a communication unit D12. The monitoring control device D1 may also have an input unit (e.g., a keyboard, a mouse, etc.) that receives various operations from an operator O, and a display unit (e.g., a liquid crystal display, etc.) that displays various information.

[0184] (2-1-1. Executive Section D11) The execution unit D11 executes measures against troubles caused by underwater particles P. For example, the execution unit D11 executes powdered activated carbon injection control by transmitting a control signal to inject powdered activated carbon P9 to a powdered activated carbon injection facility according to an injection rate of powdered activated carbon P9 calculated by an analysis unit D51 of an underwater particle countermeasure support device D5, which will be described later.

[0185] (2-1-2. Communication section D12) The communication unit D12 manages data communication with other devices. For example, the communication unit D12 performs data communication with each communication device via a router, etc. The communication unit D12 can also perform data communication with an operator's terminal (not shown).

[0186] (2-2. Configuration and processing example of underwater particle countermeasure support device D5) A configuration example and a processing example of the underwater particle countermeasure support device D5 will be described. The underwater particle countermeasure support device D5 is a server device having an analysis unit D51, an analysis result database D52, and a communication unit D53. The underwater particle countermeasure support device D5 may have an input unit that receives various operations from an operator O and a display unit that displays various information. Also, the configuration example and the processing example of the analysis result database D52 are the same as those of the first embodiment, so the description will be omitted.

[0187] (2-2-1. Analysis Department D51) Here, the analysis unit D51 can be realized by, for example, electronic circuits such as a CPU or MPU, or integrated circuits such as an ASIC or FPGA. Based on the measurement results of the underwater particles P transmitted from the underwater particle image analyzer D3, the analysis unit D51 outputs analysis results used for measures against troubles caused by the underwater particles P. Below, the odorant assumed concentration calculation process and the powdered activated carbon injection rate calculation process are described after explaining the odor and taste countermeasures of the underwater particle countermeasure support system 100P according to the reference technology.

[0188] (2-2-1-1. Taste and odor countermeasures using the Underwater Particle Countermeasure Support System 100P) The following describes the odor and taste countermeasures of the underwater particle countermeasure support system 100P. As described above in [1. Configuration and processing of the underwater particle countermeasure support system 100-1] (1-3. Effects of the underwater particle countermeasure support system 100-1) (1-3-1. Overview of the underwater particle countermeasure support system 100P) of the first embodiment, in the underwater particle countermeasure support system 100P according to the reference technology, the implementer of the algae countermeasure measures measures the water quality (turbidity, pH, alkalinity, water temperature) S1 of the raw water W2 at the receiving well P2. At this time, the implementer performs a sensory test (odor test) or GC / MS (gas chromatography mass spectrometry) to detect odorous substances (2-methylisoborneol, geosmin, etc.), or a biological survey (mainly algae) using a microscope, and if a certain amount or more of odor, odorous substances, or algae causing odor are detected, powdered activated carbon P9 is injected from the powdered activated carbon injection equipment. As described above, in the underwater particle countermeasure support system 100P, when a certain level or more of odorous and / or tasteless substances or algae that cause them are detected, the powdered activated carbon P9 is injected to take countermeasures against the odorous and / or tasteless substances.

[0189] However, in the underwater particle countermeasure support system 100P, since the operator cannot make a judgment in real time, it is difficult to control the injection in response to the increase or decrease of odor or causative algae. For example, in the underwater particle countermeasure support system 100P, when the amount of powdered activated carbon P9 injected is large, the cost increases. On the other hand, in the underwater particle countermeasure support system 100P, when the amount of powdered activated carbon P9 injected is small, an unpleasant odor or taste occurs.

[0190] (2-2-1-2. Calculation process of assumed concentration of odor substances) The analysis unit D51 executes an odorant assumed concentration calculation process. For example, the analysis unit D51 calculates an odorant assumed concentration indicating the degree of odor generation using the measurement result M transmitted from the underwater particle image analysis device D3. The calculation theory of the odorant assumed concentration calculation process and the odorant assumed concentration graph output process will be described below.

[0191] (Calculation theory of odor substance assumed concentration calculation process) The calculation theory of the odorant assumed concentration calculation process will be described. The analysis unit D51 uses the following formula (1) to calculate the odorant assumed concentration, which indicates the degree of odor generation, from the particle number M, which is the measurement result M of underwater particles P such as algae that cause odor, and the odorant content rate.

[0192]

number

[0193] In the above formula (1), "I x " indicates the mass (ng / L) of each odorant per 1L of sample water W as the assumed concentration of each odorant. k " indicates the number of particles (cell / mL) per 1 L of measurement water W, as the number of particles P of underwater particles such as algae that generate each odor substance. k " indicates the mass (ng / cell) of each odorous substance per particle as the concentration of each odorous substance. "C" indicates a correction coefficient, which is calculated from (1) above and the experimental values ​​of each odorous substance by GC / MS, etc. "Σ" indicates the total value.

[0194] That is, the analysis unit D51 calculates the odorant concentration (A ) of each underwater particle P of a predetermined odorant from the classification result of the underwater particles P and the particle number of the underwater particles P output by the measurement unit D32 of the underwater particle image analyzer D3, the concentration of each odorant indicated by the underwater particle characteristics, which are underwater particle data obtained from the underwater particle data server D4, and the correction coefficient C. k ×B k ) × C. Then, the analysis unit D51 calculates the odorant concentration (A k ×B k ) × C, the expected odorant concentration I of the sample water W for a given odorant is calculated. x Calculate.

[0195] (Odor substance assumed concentration graph output processing) The assumed odorant concentration graph output process will be described. The analysis unit D51 executes the assumed odorant concentration graph output process. For example, the analysis unit D51 outputs the analysis result A including an assumed odorant concentration graph A4, which is a graph showing the degree of odor generation calculated using the measurement result M for each of a plurality of sample waters W collected at each of a plurality of measurement positions. That is, the analysis unit D51 outputs a measurement location graph showing the assumed odorant concentration for each measurement position of the sample water W as the assumed odorant concentration graph A4. At this time, the analysis unit D51 stores the measurement location graph of the output assumed odorant concentration graph A4 in the analysis result database D52.

[0196] Furthermore, the analysis unit D51 outputs the analysis result A including an assumed odorant concentration graph A4, which is a graph showing the degree of odor generation calculated using the measurement result M, for each of the multiple sample waters W collected at each of the multiple measurement times. That is, the analysis unit D51 outputs a time-series trend graph showing the assumed odorant concentration for each measurement time of the sample water W as the assumed odorant concentration graph A4, which is the analysis result A. At this time, the analysis unit D51 stores the output time-series trend graph of the assumed odorant concentration graph A4 in the analysis result database D52.

[0197] Here, an example of the assumed odorant concentration graph A4 will be described with reference to Fig. 15. Fig. 15 is a diagram showing an example of the assumed odorant concentration graph A4 of the underwater particle countermeasure support device D5 according to embodiment 2. In the following, "assumed odorant concentration / measurement location", which is a measurement location graph of the assumed odorant concentration graph A4, and "assumed odorant concentration trend", which is a time-series trend graph of the assumed odorant concentration graph A4, will be described.

[0198] An example of "assumed odorant concentration / measurement location" is a measurement location graph of odorant assumed concentration graph A4, which shows a line graph of the mass (ng / L) per 1L of measured water W for each odorant that is a contributing substance of "geosmin" and "2-MIB" for the water testing points of dam intake water W1, raw water W2, submerged water W3, filtered water W4, and purified water W5.

[0199] An example of the "odor substance assumed concentration trend" is a time-series trend graph of odor substance assumed concentration graph A4, which shows the mass (ng / L) per 1L of measured water W for each odor substance that is a contributing substance of "geosmin" and "2-MIB" in a line graph for the five alternate days of "August 1st," "August 2nd," "August 3rd," "August 4th," and "August 5th."

[0200] (2-2-1-3. Powdered activated carbon injection rate calculation process) The analysis unit D51 executes a powdered activated carbon injection rate calculation process. For example, the analysis unit D51 calculates a powdered activated carbon injection rate indicating the injection rate of the powdered activated carbon P9 based on the measurement result M transmitted from the underwater particle image analysis device D3. The calculation theory of the powdered activated carbon injection rate calculation process will be described below.

[0201] (Calculation theory for powdered activated carbon injection rate calculation process) The calculation theory of the powdered activated carbon injection rate calculation process will be described. The analysis unit D51 calculates the powdered activated carbon injection rate, which indicates the injection rate of the powdered activated carbon P9, from the assumed odorant concentration calculated based on the measurement results M of underwater particles P such as algae that generate odorants, using the following formulas (2) and (3).

[0202]

number

[0203]

number

[0204] In the above formulas (2) and (3), "R" indicates the mass (mg / L) of powdered activated carbon P9 injected per 1 L of the sample water W as the activated carbon injection rate. "T" indicates the temperature (°C) of the sample water W. n " indicates the mass (ng / L) of odorant per 1L of sample water W as the initial concentration of the odorant. n" indicates the mass (ng / L) of odorant per 1L of sample water W as the target concentration of the odorant. n " and "b n " indicates a constant, "ln" indicates the natural logarithm, and "Σ" indicates a sum.

[0205] The above formulas (2) and (3) were obtained from the relationship between the odorant residual rate and the relationship between the water temperature and the odorant residual rate through experiments using the powdered activated carbon P9 used. The above formula (2) is applied when the odorant is in low concentration. On the other hand, the above formula (3) is applied when the odorant is in high concentration and there are many components other than the odorant.

[0206] That is, the analysis unit D51 calculates the odorant assumed concentration I of each odorant in the sample water W by the above-mentioned odorant assumed concentration calculation process. x Then, when the concentration of each odorant is low, the analysis unit D51 calculates the odorant assumed concentration I x Let I be the initial concentration n The measured water temperature T and the target concentration F of the odorant assumed concentration set n , and the correction factor a n and b n On the other hand, when each odorant is in a high concentration and there are many components other than the odorant, the analysis unit D51 calculates the activated carbon injection rate R using the above formula (2). x Let I be the initial concentration n The measured water temperature T and the target concentration F of the odorant assumed concentration set n , and the correction factor a n and b n The activated carbon injection rate R is calculated by adding up the injection rates of the powdered activated carbon P9 required for each odorant using the above formula (3). At this time, the analysis unit D51 can also take in experimental values ​​of components other than odorants as correction values.

[0207] (2-2-2. Communication section D53) The communication unit D53 manages data communication with other devices. For example, the communication unit D53 performs data communication with each communication device via a router, etc. The communication unit D53 can also perform data communication with an operator's terminal (not shown).

[0208] (2-3. Example of the configuration and processing of display terminal D6) A configuration example and a processing example of the display terminal D6 will be described with reference to Fig. 6 again. The display terminal D6 is a terminal used by an operator O who is a manager who manages the sample water W, and is a manager terminal having an input / output unit D61, a control unit D62, and a communication unit D63.

[0209] (2-3-1. Input / output section D61) The input / output unit D61 controls input of various information to the display terminal D6. For example, the input / output unit D61 is realized by a mouse, a keyboard, a touch panel, etc., and accepts input of setting information, etc. to the display terminal D6. The input / output unit D61 also controls display of various information from the display terminal D6. For example, the input / output unit D61 is realized by a display, etc., and displays setting information, etc. stored in the display terminal D6.

[0210] Furthermore, the input / output unit D61 displays the analysis result A transmitted from the underwater particle countermeasure support device D5. For example, the input / output unit D61 displays, as the analysis result A, the assumed odorant concentration, the assumed odorant concentration graph A4 (measurement point graph, time-series trend graph), the powdered activated carbon injection rate, etc.

[0211] (2-3-2. Control unit D62) The control unit D62 transmits various information. The control unit D62 also receives various information. For example, the control unit D62 receives the analysis result A from the underwater particle countermeasure support device D5.

[0212] (2-3-3. Communication section D63) The communication unit D63 manages data communication with other devices. For example, the communication unit D63 performs data communication with each communication device via a router, etc. The communication unit D63 can also perform data communication with an operator's terminal (not shown).

[0213] 3. Effects of the Second Embodiment Finally, a description will be given of the effects of the second embodiment. The following describes effects 1 to 4 corresponding to the processing according to the second embodiment.

[0214] (3-1. Effect 1) First, in the process according to the above-mentioned embodiment 2, the underwater particle countermeasure support device D5 outputs the analysis result A including the measurement location graph of the odorant assumed concentration graph A4 indicating the odor generation degree calculated using the measurement result M for each of the multiple sample waters W collected at each of the multiple measurement locations. Therefore, in this process, by automatically outputting the analysis result A including the odorant assumed concentration for each measurement location, it is possible to efficiently implement countermeasures against unpleasant odors and tastes among the problems caused by underwater particles P.

[0215] (3-2. Effect 2) Secondly, in the process according to the above-mentioned embodiment 2, the underwater particle countermeasure support device D5 outputs the analysis result A including a time-series trend graph of the odorant assumed concentration graph A4 indicating the degree of odor generation calculated using the measurement result M for each of the multiple sample waters W collected at each of the multiple measurement times. Therefore, in this process, by automatically outputting the analysis result A including the odorant assumed concentration for each measurement time, it is possible to efficiently implement countermeasures against unpleasant odors and tastes among the problems caused by the underwater particles P.

[0216] (3-3. Effect 3) Thirdly, in the process according to the above-mentioned embodiment 2, the underwater particle countermeasure support device D5 outputs the analysis result A including the powdered activated carbon injection rate, which is the injection rate of the powdered activated carbon P9 calculated using the measurement result M. Therefore, in this process, by automatically outputting the analysis result A including the injection rate of the powdered activated carbon P9 according to the odorous substance, it is possible to efficiently implement countermeasures against unpleasant odors and tastes among the problems caused by the underwater particles P.

[0217] (3-4. Effect 4) Fourthly, in the process according to the above-mentioned embodiment 2, the monitoring and controlling device D1 controls the injection of the powdered activated carbon P9 into the sample water W in accordance with the powdered activated carbon injection rate, which is the injection rate of the powdered activated carbon P9 indicated by the analysis result A. Therefore, in this process, the injection of the powdered activated carbon P9 into the sample water W in accordance with the odorous substance is automatically performed, thereby making it possible to efficiently take measures against unpleasant odors and tastes, which are among the problems caused by the particles P in the water.

[0218] [Embodiment 3] Below, the configuration and processing of underwater particle countermeasure support system 100-3 according to embodiment 3, the configuration and processing of each device of underwater particle countermeasure support system 100-3 will be described in order, and finally the effects of embodiment 3 will be described. Note that descriptions of configurations and processing common to underwater particle countermeasure support system 100-1 according to embodiment 1 or underwater particle countermeasure support system 100-2 according to embodiment 2 will be omitted.

[0219] 1. Configuration and Processing of Underwater Particle Countermeasure Support System 100-3 The configuration and processing of underwater particle countermeasure support system 100-3 according to embodiment 3 will be described in detail with reference to Fig. 16. Fig. 16 is a diagram showing an example of the configuration and processing of underwater particle countermeasure support system 100-3 according to embodiment 3. An example of the overall processing of underwater particle countermeasure support system 100-3 will be described below. Note that the example of the overall configuration of underwater particle countermeasure support system 100-3 is common to the example of the overall configuration of underwater particle countermeasure support system 100-1, so description thereof will be omitted.

[0220] (1-1. Example of overall processing of underwater particle countermeasure support system 100-3) An example of the overall processing of the underwater particle countermeasure support system 100-3 will be described with reference to Fig. 16. Note that the processes in Fig. 16(31)-(35) below can be executed in a different order. Also, some of the processes in Fig. 16(31)-(35) below may be omitted. Also, the process prior to the process in Fig. 16(31) below is the same as the processes in Fig. 1(1)-(8) described in the first embodiment, so the description thereof will be omitted.

[0221] (1-1-1. Aqueous particle analysis treatment in water with inhibition of agglomeration) First, the underwater particle countermeasure support device D5 analyzes underwater particles P related to coagulation inhibition (see FIG. 16 (31)). For example, the underwater particle countermeasure support device D5 calculates the injection rate of coagulant P11 by coagulation-sedimentation simulation processing using the measurement results M of the underwater particles P transmitted from the underwater particle image analyzer D3, and outputs the calculated coagulant injection rate as the analysis result A. Furthermore, the underwater particle countermeasure support device D5 calculates the injection rate of pH adjuster P10 by coagulation-sedimentation simulation processing using the measurement results M of the underwater particles P transmitted from the underwater particle image analyzer D3, and outputs the calculated pH adjuster injection rate as the analysis result A.

[0222] (1-1-2. Agglutination inhibition analysis result transmission processing) Secondly, the underwater particle countermeasure support device D5 transmits the analysis result A regarding the coagulation inhibition to the display terminal D6 (see FIG. 16 (32)). For example, the underwater particle countermeasure support device D5 transmits the coagulant injection rate, the pH adjuster injection rate, and the like as the analysis result A to the display terminal D6.

[0223] (1-1-3. Display processing of aggregation inhibition analysis results) Thirdly, the display terminal D6 displays the analysis result A regarding the coagulation inhibition (see FIG. 16(33)). For example, the display terminal D6 displays the coagulant injection rate, the pH adjuster injection rate, etc. as the analysis result A on the monitor screen.

[0224] (1-1-4. Input processing for countermeasures against coagulation-inhibiting water particles) Fourth, the operator O inputs to the monitoring and controlling device D1 an underwater particle countermeasure related to coagulation inhibition (see FIG. 16 (34)). For example, the operator O inputs to the monitoring and controlling device D1 an instruction to execute the underwater particle countermeasure of injecting a coagulant based on the analysis result A displayed on the monitor screen of the display terminal D6. For example, the operator O inputs to the monitoring and controlling device D1 an instruction to execute the underwater particle countermeasure of injecting a pH adjuster based on the analysis result A displayed on the monitor screen of the display terminal D6.

[0225] (1-1-5. Processing to deal with coagulation-inhibiting particles in water) Fifth, the monitoring and control device D1 implements measures against coagulation-inhibiting underwater particles (see FIG. 16 (35)). For example, the monitoring and control device D1 implements measures against coagulation-inhibiting underwater particles among the measures against underwater particles in the water purification facility by implementing coagulant injection control of the coagulant injection equipment according to the calculated coagulant injection rate. Also, the monitoring and control device D1 implements measures against coagulation-inhibiting underwater particles among the measures against underwater particles in the water purification facility by implementing pH adjuster injection control of the pH adjuster injection equipment according to the calculated pH adjuster injection rate.

[0226] 2. Configuration and Processing of Each Device of Underwater Particle Countermeasure Support System 100-3 Referring again to FIG. 6, the configuration and processing of each device of the underwater particle countermeasure support system 100-3 shown in FIG. 16 will be described. Note that the configuration example of each device of the underwater particle countermeasure support system 100-3 according to the third embodiment is common to the configuration example of each device of the underwater particle countermeasure support system 100-1 according to the first embodiment shown in FIG. 6. Below, the configuration example and processing example of the monitoring control device D1, the configuration example and processing example of the underwater particle countermeasure support device D5, and the configuration example and processing example of the display terminal D6 according to the third embodiment will be described in detail. Note that the configuration example of the entire underwater particle countermeasure support system 100-3 according to the third embodiment, the configuration example and processing example of the sampling device D2, the configuration example and processing example of the underwater particle image analysis device D3, and the configuration example and processing example of the underwater particle data server D4 are common to the first embodiment, so the description will be omitted.

[0227] (2-1. Configuration and Processing Examples of the Monitoring and Control Device D1) A configuration example and a processing example of the monitoring control device D1 will be described. The monitoring control device D1 is a control device having an execution unit D11 and a communication unit D12. The monitoring control device D1 may also have an input unit (e.g., a keyboard, a mouse, etc.) that receives various operations from an operator O, and a display unit (e.g., a liquid crystal display, etc.) that displays various information.

[0228] (2-1-1. Executive Section D11) The execution unit D11 executes measures against the troubles caused by the underwater particles P. For example, the execution unit D11 executes the flocculant injection control by sending a control signal to the flocculant injection equipment to inject the flocculant P11 according to the injection rate of the flocculant P11 calculated by the analysis unit D51 of the underwater particle countermeasures support device D5 described later. Also, the execution unit D11 executes the pH adjuster injection control by sending a control signal to the pH adjuster injection equipment to inject the pH adjuster according to the injection rate of the pH adjuster P10 calculated by the analysis unit D51 of the underwater particle countermeasures support device D5 described later.

[0229] (2-1-2. Communication section D12) The communication unit D12 manages data communication with other devices. For example, the communication unit D12 performs data communication with each communication device via a router, etc. The communication unit D12 can also perform data communication with an operator's terminal (not shown).

[0230] (2-2. Configuration and processing example of underwater particle countermeasure support device D5) A configuration example and a processing example of the underwater particle countermeasure support device D5 will be described. The underwater particle countermeasure support device D5 is a server device having an analysis unit D51, an analysis result database D52, and a communication unit D53. The underwater particle countermeasure support device D5 may have an input unit that receives various operations from an operator O and a display unit that displays various information. Also, the configuration example and the processing example of the analysis result database D52 are the same as those of the first embodiment, so the description will be omitted.

[0231] (2-2-1. Analysis Department D51) Here, the analysis unit D51 can be realized by, for example, electronic circuits such as a CPU or MPU, or integrated circuits such as an ASIC or FPGA. Based on the measurement results of the underwater particles P transmitted from the underwater particle image analyzer D3, the analysis unit D51 outputs an analysis result A used for taking measures against troubles caused by the underwater particles P. Below, the coagulation inhibition countermeasures of the underwater particle countermeasure support system 100P according to the reference technology are explained, and then the calculation theory of the coagulant injection rate calculation process and the pH adjuster injection rate calculation process, the coagulant injection rate calculation process, and the pH adjuster injection rate calculation process are explained.

[0232] (2-2-1-1. Countermeasures against coagulation inhibition using the underwater particle countermeasure support system 100P) The coagulation inhibition countermeasures of the underwater particle countermeasure support system 100P will be described. As described above in [1. Configuration and processing of the underwater particle countermeasure support system 100-1] (1-3. Effects of the underwater particle countermeasure support system 100-1) (1-3-1. Outline of the underwater particle countermeasure support system 100P) of the first embodiment, in the underwater particle countermeasure support system 100P according to the reference technology, the implementer of the algae countermeasure measures measures the water quality (turbidity, pH, alkalinity, water temperature) S1 of the raw water W2 in the receiving well P2. At this time, the implementer injects a pH adjuster P10 such as caustic soda or sulfuric acid from the pH adjuster injection equipment to make the pH suitable for coagulation. In addition, the implementer injects a coagulant P11 such as PAC (polyaluminum chloride) or aluminium sulfate in the chemical mixing pond P3 and rapidly stirs it. At this time, the implementer calculates the coagulant injection rate based on the raw water turbidity S1. In addition, since the properties of water differ depending on the water purification facility, the practitioner derives the relational equation from experiments and experience. In addition, since the optimal injection rate varies depending on the season and weather conditions, the practitioner performs a jar test (a test of injection of coagulant and pH adjuster using a beaker) to determine the injection rate. In addition, in the flocculation tank P4, the practitioner grows the turbidity into large flocs by slow stirring. At this time, the practitioner detects the state of the flocs using a sensor and uses the result for feedback control. In addition, in the sedimentation tank P5, the practitioner settles and removes the large flocs. Here, the settling velocity v sAccording to Stokes' law, is proportional to the square of the particle diameter and the density difference with water. At this time, the operator measures the turbidity S2 at the outlet of the settling tank and utilizes it for feedback control.

[0233] However, in the underwater particle countermeasure support system 100P, the coagulant injection rate is calculated based on the raw water turbidity S1, but even with the same turbidity, various types and sizes of phytoplankton flow in, and the chemical injection rate often does not match. Therefore, in the underwater particle countermeasure support system 100P, when there is a change in the water quality of the raw water W2, the chemical injection rate is determined by performing a jar test, which requires labor and causes delays in control.

[0234] (2-2-1-2. Calculation theory of coagulant injection rate calculation process and pH adjuster injection rate calculation process) The calculation theory of the flocculant injection rate calculation process and the pH adjuster injection rate calculation process will be described. The analysis unit D51 calculates at least one of the flocculant injection rate and the pH adjuster injection rate from the measurement result M of the underwater particles P such as algae that cause the occurrence of flocculation inhibition, using the following formulas (A) to (F) and (4) and the calculation means shown in Fig. 17.

[0235] P=X 1 ×T n1 ×A n2 ×K n3 ×H n4 (A)

[0236] P=X 2 ×T+X 3 ×A (B)

[0237] P=X 2 ×T+X 3 ×A+X 4 ×S (C)

[0238] P=X 2 ×T n1 X 3 ×AX 5 (D)

[0239] P=X 1 ×(A×K)n5 ×T n1 (E)

[0240] P=X 1 xAxH n4 ×T n1 (F)

[0241] In the above formulas (A) to (F), "P" indicates the injection rate converted to solid band. "T" indicates the raw water turbidity. "A" indicates the raw water alkalinity. "K" indicates the amount of potassium permanganate consumed in the raw water. "H" indicates the raw water pH. "S" indicates the injection rate that leaves an alkalinity of 10 to 15 mg / L. "X" indicates the amount of potassium permanganate consumed in the raw water. 1 ", "X 2 ", "X 3 ", "X 4 " and "X 5 " indicates a constant. Also, "n 1 ", "n 2 ", "n 3 ", "n 4 " and "n 5 " indicates a value determined by experiment.

[0242]

number

[0243] In the Stokes equation (4) above, "v s " indicates the settling velocity of the particle (m / s). p " indicates the particle diameter (m). Also, "ρ p " is the density of the particle (kg / m 3 ) and "ρ f " is the density of the fluid (kg / m 3 ) and "g" stands for gravitational acceleration (m / s 2 ) and "η" indicates the viscosity of the fluid (Pa s).

[0244] The above formulas (A) to (F) and (4) can also be used in the underwater particle countermeasure support system 100P according to the reference technology. However, in the underwater particle countermeasure support system 100P, since it is difficult to measure the particle size distribution and specific gravity (density difference from water) of the raw water W2, an estimated value is used for the particle size distribution of the turbidity components, and the specific gravity is not taken into consideration. In the underwater particle countermeasure support system 100-3, the analysis unit D51 can automatically measure the particle size distribution (particle diameter) of the raw water W2 and the proportion (specific gravity) of underwater particles P such as algae as the measurement result M of the underwater particles P, so that it is possible to execute a coagulation and sedimentation simulation process taking into consideration the particle size distribution and specific gravity of the raw water W2.

[0245] Here, the coagulation sedimentation simulation process executed by the analysis unit D51 will be described with reference to Fig. 17. Fig. 17 is a diagram showing an example of the coagulation sedimentation simulation process of the underwater particle countermeasure support device D5 according to embodiment 3. In the following, "ion equilibrium", "turbidity", "turbidity removal", and "floc formation" will be described as calculation means of the coagulation sedimentation simulation process executed by the analysis unit D51.

[0246] (Ionic equilibrium) The analysis unit D51 calculates pH, alkalinity, etc. from the water quality and the ion balance in the water when each chemical is added as a coagulation and precipitation simulation process related to ion equilibrium. At this time, the analysis unit D51 calculates ion concentration (e.g., H + , O.H. - , CO 2aq ), hypochlorous acid concentration, caustic concentration, PAC concentration, etc. are input. In addition, the analysis unit D51 outputs pH, alkalinity, each ion concentration, etc. as output variables. In addition, the analysis unit D51 realizes a coagulation precipitation simulation process related to ion equilibrium using an ion equilibrium calculation formula.

[0247] (Turbidity) The analysis unit D51 calculates turbidity from the turbidity concentration and turbidity particle size distribution in water as a coagulation and sedimentation simulation process related to turbidity. At this time, the analysis unit D51 inputs the turbidity concentration, the turbidity particle size distribution, the turbidity specific gravity estimated from the measurement result M, and the like as input variables. The analysis unit D51 also outputs the turbidity and the like as output variables. The analysis unit D51 also realizes the coagulation and sedimentation simulation process related to turbidity by using the above formulas (A) to (F) and by regarding the turbidity concentration as turbidity particle size distribution data.

[0248] (turbidity removal) The analysis unit D51 calculates the turbidity removal rate from the pH, alkalinity, and PAC injection rate as a coagulation and sedimentation simulation process related to turbidity removal. At this time, the analysis unit D51 inputs the pH, alkalinity, PAC injection rate, etc. as input variables. The analysis unit D51 also outputs the turbidity removal rate, etc. as output variables. The analysis unit D51 also realizes the coagulation and sedimentation simulation process related to turbidity by using performance data.

[0249] (Floc formation) The analysis unit D51 calculates the change in particle size distribution of flocs from the turbidity concentration and turbidity particle size distribution as a coagulation and sedimentation simulation process for turbidity. At this time, the analysis unit D51 inputs the turbidity concentration, turbidity particle size distribution, etc. as input variables. The analysis unit D51 also outputs the turbidity concentration, turbidity particle size distribution, turbidity specific gravity, etc. as output variables. The analysis unit D51 also realizes the coagulation and sedimentation simulation process for turbidity by using performance data.

[0250] That is, the analysis unit D51 executes the above-mentioned coagulation and sedimentation simulation process to associate the particle size distribution and classification results of the raw water obtained from the underwater particle image analyzer D3 with the underwater particle characteristics of the underwater particles P obtained from the underwater particle data server D4, and enables a coagulation and sedimentation simulation that takes into account the particle diameter and specific gravity (density difference with water) in Stokes' equation. Also, by reconverting the carryover particles into turbidity, the turbidity at the outlet of the settling tank P5 is calculated, and the coagulant injection rate that minimizes this value is calculated. Also, the analysis unit D51 calculates the pH adjuster injection rate to make the pH suitable for coagulation.

[0251] (2-2-1-3. Flocculant injection rate calculation process) The analysis unit D51 executes a flocculant injection rate calculation process. For example, the analysis unit D51 calculates the injection rate of the flocculant P11 using the measurement result M transmitted from the underwater particle image analysis device D3, and outputs the analysis result A including the calculated flocculant injection rate. At this time, the analysis unit D51 calculates the injection rate of the flocculant P11 by executing the above-mentioned flocculation and sedimentation simulation process using the particle size distribution of the underwater particles P, the classification result of the type of the underwater particles P, and the specific gravity of the underwater particles P as the measurement result M.

[0252] (2-2-1-4. pH adjuster injection rate calculation process) The analysis unit D51 executes a pH adjuster injection rate calculation process. For example, the analysis unit D51 calculates the injection rate of the pH adjuster P10 using the measurement result M transmitted from the underwater particle image analyzer D3, and outputs an analysis result A including the calculated pH adjuster injection rate. At this time, the analysis unit D51 calculates the injection rate of the pH adjuster P10 by executing the above-mentioned coagulation and sedimentation simulation process using the particle size distribution of the underwater particles P, the classification result of the type of the underwater particles P, and the underwater particle characteristics such as the specific gravity of the underwater particles P as the measurement result M.

[0253] (2-2-2. Communication section D53) The communication unit D53 manages data communication with other devices. For example, the communication unit D53 performs data communication with each communication device via a router, etc. The communication unit D53 can also perform data communication with an operator's terminal (not shown).

[0254] (2-3. Example of the configuration and processing of display terminal D6) A configuration example and a processing example of the display terminal D6 will be described with reference to Fig. 6 again. The display terminal D6 is a terminal used by an operator O who is a manager who manages the sample water W, and is a manager terminal having an input / output unit D61, a control unit D62, and a communication unit D63.

[0255] (2-3-1. Input / output section D61) The input / output unit D61 controls input of various information to the display terminal D6. For example, the input / output unit D61 is realized by a mouse, a keyboard, a touch panel, etc., and accepts input of setting information, etc. to the display terminal D6. The input / output unit D61 also controls display of various information from the display terminal D6. For example, the input / output unit D61 is realized by a display, etc., and displays setting information, etc. stored in the display terminal D6.

[0256] Furthermore, the input / output unit D61 displays the analysis result A transmitted from the underwater particle countermeasure support device D5. For example, the input / output unit D61 displays the coagulant injection rate, the pH adjuster injection rate, and the like as the analysis result A.

[0257] (2-3-2. Control unit D62) The control unit D62 transmits various information. The control unit D62 also receives various information. For example, the control unit D62 receives the analysis result A from the underwater particle countermeasure support device D5.

[0258] (2-3-3. Communication section D63) The communication unit D63 manages data communication with other devices. For example, the communication unit D63 performs data communication with each communication device via a router, etc. The communication unit D63 can also perform data communication with an operator's terminal (not shown).

[0259] 3. Effects of the Third Embodiment Finally, a description will be given of the effects of the third embodiment. The following describes effects 1 to 4 corresponding to the processing according to the third embodiment.

[0260] (3-1. Effect 1) First, in the process according to the above-mentioned embodiment 3, the underwater particle countermeasure support device D5 outputs the analysis result A including the flocculant injection rate, which is the injection rate of the flocculant P11 calculated using the measurement result M. Therefore, in this process, by automatically outputting the analysis result A including the injection rate of the flocculant P11 according to the flocculation inhibition particles, it is possible to efficiently implement countermeasures against flocculation inhibition, which is one of the problems caused by the underwater particles P.

[0261] (3-2. Effect 2) Secondly, in the process according to the above-mentioned embodiment 3, the underwater particle countermeasure support device D5 outputs the analysis result A including the pH adjuster injection rate, which is the injection rate of the pH adjuster P10 calculated using the measurement result M. Therefore, in this process, by automatically outputting the analysis result A including the injection rate of the pH adjuster P10 according to the agglutination inhibition particles, it is possible to efficiently implement countermeasures against agglutination inhibition, which is one of the problems caused by the underwater particles P.

[0262] (3-3. Effect 3) Thirdly, in the process according to the above-mentioned embodiment 3, the monitoring and control device D1 controls the injection of the flocculant P11 into the sample water W in accordance with the flocculant injection rate, which is the injection rate of the flocculant P11 indicated by the analysis result A. Therefore, in this process, by automatically injecting the flocculant P11 into the sample water W in accordance with the flocculation inhibition particles, it is possible to efficiently take measures against the flocculation inhibition, which is one of the problems caused by the underwater particles P.

[0263] (3-4. Effect 4) Fourthly, in the process according to the above-mentioned embodiment 3, the monitoring and controlling device D1 controls the injection of the pH adjusting agent P10 into the sample water W in accordance with the pH adjusting agent injection rate, which is the injection rate of the pH adjusting agent P10 indicated by the analysis result A. Therefore, in this process, by automatically injecting the pH adjusting agent P10 into the sample water W in accordance with the agglutination inhibitor particles, it is possible to efficiently take measures against agglutination inhibition, which is one of the problems caused by the particles P in the water.

[0264] [Embodiment 4] Below, the configuration and processing of underwater particle countermeasure support system 100-4 according to embodiment 4, the configuration and processing of each device of underwater particle countermeasure support system 100-4 will be described in order, and finally the effects of embodiment 4 will be described. Note that descriptions of configurations and processing common to underwater particle countermeasure support system 100-1 according to embodiment 1, underwater particle countermeasure support system 100-2 according to embodiment 2, or underwater particle countermeasure support system 100-3 according to embodiment 3 will be omitted.

[0265] 1. Configuration and Processing of Underwater Particle Countermeasure Support System 100-4 The configuration and processing of underwater particle countermeasure support system 100-4 according to the fourth embodiment will be described in detail with reference to Fig. 18. Fig. 18 is a diagram showing an example of the configuration and processing of underwater particle countermeasure support system 100-4 according to the fourth embodiment. An example of the overall processing of underwater particle countermeasure support system 100-4 and the effects of underwater particle countermeasure support system 100-4 will be described below. Note that the example of the overall configuration of underwater particle countermeasure support system 100-4 is common to the example of the overall configuration of underwater particle countermeasure support system 100-1, so description thereof will be omitted.

[0266] (1-1. Example of overall processing of underwater particle countermeasure support system 100-4) An example of the overall processing of the underwater particle countermeasure support system 100-4 will be described with reference to Fig. 18. Note that the processes in Fig. 18 (41) to (44) below can be executed in a different order. Also, some of the processes in Fig. 18 (41) to (44) below may be omitted. Also, the process prior to the process in Fig. 18 (41) below is the same as the processes in Fig. 1 (1) to (8) described in the first embodiment, so the description thereof will be omitted.

[0267] (1-1-1. Analysis of particles in water with filtration obstruction) First, the underwater particle countermeasure support device D5 analyzes underwater particles P related to filtration failures (filter blockage, filter leakage, unpleasant odors and tastes) (see FIG. 18 (41)). For example, the underwater particle countermeasure support device D5 predicts the head loss (m) of the rapid filtration basin P6, which shows a filter resistance (filter resistance), by a filtration simulation process using the measurement results M of the underwater particles P transmitted from the underwater particle image analyzer D3, regarding filtration blockage among filtration failures, and outputs the predicted head loss (m) as the analysis result A. Furthermore, the underwater particle countermeasure support device D5 predicts the filtration output (cell / L) by a filtration simulation process using the measurement results M of the underwater particles P transmitted from the underwater particle image analyzer D3, regarding filtration leakage among filtration failures, and outputs the predicted filtration output (cell / L) as the analysis result A. In addition, with regard to unpleasant odors and tastes that are part of filtration problems, the underwater particle countermeasures support device D5 predicts the expected concentration (ng / L) of odorous substances in the filtered water W4 through a filtration simulation process using the measurement results M of the underwater particles P transmitted from the underwater particle image analysis device D3, and outputs the predicted expected concentration (ng / L) of odorous substances as the analysis result A.

[0268] (1-1-2. Filtration failure analysis result transmission process) Secondly, the underwater particle countermeasure support device D5 transmits an analysis result A relating to the filtration failure to the display terminal D6 (see FIG. 18 (42)). For example, the underwater particle countermeasure support device D5 transmits, as the analysis result A, a time series trend graph of the predicted value of head loss (m), a time series trend graph of the predicted value of filtration output (cell / L), a time series trend graph of the predicted value of the assumed odorant concentration (ng / L), etc. to the display terminal D6. In addition, the underwater particle countermeasure support device D5 transmits, as the analysis result A, the predicted value of head loss (m), the predicted value of filtration output (cell / L), the predicted value of the assumed odorant concentration (ng / L), etc. to the monitoring control device D1.

[0269] (1-1-3. Filtration failure analysis result display processing) Thirdly, the display terminal D6 displays the analysis result A regarding the filtration failure (see FIG. 18 (43)). For example, the display terminal D6 displays, as the analysis result A, a time series trend graph of the predicted value of the head loss (m), a time series trend graph of the predicted value of the filtration output (cell / L), a time series trend graph of the predicted value of the assumed odorous substance concentration (ng / L), etc. on the monitor screen.

[0270] (1-1-4. Filtration failure prediction alarm notification processing) Fourth, the monitoring control device D1 executes a filtration failure prediction alarm notification process (see FIG. 18 (44)). For example, when the predicted value of the head loss (m) exceeds the filtration blockage control value, the monitoring control device D1 notifies the operator O of a prediction alarm of the occurrence of filtration blockage. Furthermore, when the predicted value of the filtration output (cell / L) exceeds the filtration leakage control value, the monitoring control device D1 notifies the operator O of a prediction alarm of the occurrence of filtration leakage. Furthermore, when the predicted value of the assumed odorous substance concentration (ng / L) exceeds the off-odor taste / odor control value, the monitoring control device D1 notifies the operator O of a prediction alarm of the occurrence of off-odor taste / odor.

[0271] (1-2. Effects of the Underwater Particle Countermeasures Support System 100-4) The effect of the underwater particle countermeasure support system 100-4 will be described. In the underwater particle countermeasure support system 100P according to the reference technology, the operator O often deals with the inflow of harmful organisms after various types of damage have become apparent. On the other hand, in the underwater particle countermeasure support system 100-4, the operator O can predict the status of the sedimentation basin P5 outlet, the rapid sand filter basin P6, etc. by inputting the dam intake water W1 or the raw water W2 through simulation calculation. In addition, in the underwater particle countermeasure support system 100-4, the operator O can predict future behavior based on the predicted value of the raw water W2 (e.g., past actual value, calculation, or prediction by AI). In addition, in the underwater particle countermeasure support system 100-4, when calculation is required from the water source P1 to the receiving well P2 and from the rapid sand filter basin P6 to the purified water basin P7, the operator O can predict by calculating in the same manner as the filtration leakage simulation described later. At this time, in the underwater particle countermeasure support system 100-4, the operator O can simply adopt a fixed removal rate. In addition, in the underwater particle countermeasures support system 100-4, the operator O can also view a countermeasure-specific predicted measurement location graph that shows predicted values ​​for the measurement locations (dam intake water W1, raw water W2, submerged water W3, filtered water W4, purified water W5) for each filtration disorder (filtration blockage, filtration leakage, abnormal odors and tastes).

[0272] 2. Configuration and Processing of Each Device in Underwater Particle Countermeasure Support System 100-4 Referring again to FIG. 6, the configuration and processing of each device of the underwater particle countermeasure support system 100-4 shown in FIG. 18 will be described. Note that the configuration example of each device of the underwater particle countermeasure support system 100-4 according to the fourth embodiment is common to the configuration example of each device of the underwater particle countermeasure support system 100-1 according to the first embodiment shown in FIG. 6. Below, the configuration example and processing example of the monitoring control device D1, the configuration example and processing example of the underwater particle countermeasure support device D5, and the configuration example and processing example of the display terminal D6 according to the fourth embodiment will be described in detail. Note that the configuration example of the entire underwater particle countermeasure support system 100-4 according to the fourth embodiment, the configuration example and processing example of the sampling device D2, the configuration example and processing example of the underwater particle image analysis device D3, and the configuration example and processing example of the underwater particle data server D4 are common to the first embodiment, so the description will be omitted.

[0273] (2-1. Configuration and Processing Examples of the Monitoring and Control Device D1) A configuration example and a processing example of the monitoring control device D1 will be described. The monitoring control device D1 is a control device having an execution unit D11 and a communication unit D12. The monitoring control device D1 may also have an input unit (e.g., a keyboard, a mouse, etc.) that receives various operations from an operator O, and a display unit (e.g., a liquid crystal display, etc.) that displays various information.

[0274] (2-1-1. Executive Section D11) The execution unit D11 executes measures against troubles caused by underwater particles P. For example, the execution unit D11 executes measures against filtration troubles (filtration blockage, filtration leakage, and unpleasant taste and odor) caused by underwater particles P. Below, as the filtration trouble prediction alarm notification process, a filtration blockage prediction alarm notification process, a filtration leakage prediction alarm notification process, and an unpleasant taste and odor prediction alarm notification process will be described.

[0275] (2-1-1-1. Filtration blockage prediction alarm notification processing) The execution unit D11 issues an alarm predicting the occurrence of filtration blockage when the resistance indicated by the analysis result A of the analysis unit D51 of the underwater particle countermeasure support device D5 described later exceeds a threshold value. For example, when the predicted value of the head loss (m) which is the resistance exceeds the filtration blockage control value, the execution unit D11 issues an alarm sound via a speaker as a filtration blockage prediction alarm to notify the operator O who is the manager. At this time, the execution unit D11 can also notify the operator O of the predicted date and time when the predicted value of the head loss (m) will exceed the filtration blockage control value.

[0276] (2-1-1-2. Filtration leakage prediction alarm notification processing) The execution unit D11 issues an alarm predicting the occurrence of a filtration leakage when the filtration output indicated by the analysis result A of the analysis unit D51 of the underwater particle countermeasure support device D5 described later exceeds a threshold value. For example, when the predicted value of the filtration output (cell / L) exceeds the filtration leakage control value, the execution unit D11 notifies the operator O, who is the manager, by issuing an alarm sound via a speaker as a filtration leakage prediction alarm. At this time, the execution unit D11 can also notify the operator O of the predicted date and time when the predicted value of the filtration output (cell / L) will exceed the filtration leakage control value.

[0277] (2-1-1-3. Taste and odor prediction warning notification processing) The execution unit D11 issues an alarm predicting the occurrence of an unpleasant odor or taste when the odor occurrence level indicated by the analysis result A of the analysis unit D51 of the underwater particle countermeasure support device D5 described later exceeds a threshold value. For example, when the predicted value of the odorous substance assumed concentration (ng / L) contained in the filtered water W4 exceeds the unpleasant odor or taste control value, the execution unit D11 notifies the operator O, who is the manager, by emitting an alarm sound via a speaker as an unpleasant odor or taste prediction alarm. At this time, the execution unit D11 can also notify the operator O of the predicted date and time when the predicted value of the odorous substance assumed concentration (ng / L) will exceed the unpleasant odor or taste control value.

[0278] (2-1-2. Communication section D12) The communication unit D12 manages data communication with other devices. For example, the communication unit D12 performs data communication with each communication device via a router, etc. The communication unit D12 can also perform data communication with an operator's terminal (not shown).

[0279] (2-2. Configuration and processing example of underwater particle countermeasure support device D5) A configuration example and a processing example of the underwater particle countermeasure support device D5 will be described. The underwater particle countermeasure support device D5 is a server device having an analysis unit D51, an analysis result database D52, and a communication unit D53. The underwater particle countermeasure support device D5 may have an input unit that receives various operations from an operator O and a display unit that displays various information. Also, the configuration example and the processing example of the analysis result database D52 are the same as those of the first embodiment, so the description will be omitted.

[0280] (2-2-1. Analysis Department D51) Here, the analysis unit D51 can be realized by, for example, an electronic circuit such as a CPU or an MPU, or an integrated circuit such as an ASIC or an FPGA. The analysis unit D51 outputs an analysis result A used for taking measures against trouble caused by the underwater particles P, based on the measurement result M of the underwater particles P transmitted from the underwater particle image analyzer D3. Each process of the filtration trouble prediction process (filtration blockage prediction process, filtration leakage prediction process, and unpleasant taste and odor prediction process) will be described below.

[0281] (2-2-1-1. Filtration blockage prediction process) The analysis unit D51 executes a filtration clogging prediction process. For example, the analysis unit D51 calculates a predicted value of the head loss (m), which is a resistance, using the measurement result M transmitted from the underwater particle image analysis device D3. The prediction theory of the filtration clogging prediction process and the filtration clogging prediction graph output process will be described below.

[0282] (Prediction theory of filtration clogging prediction process) A prediction theory of the filtration blockage prediction process as the filtration failure prediction process will be described with reference to Fig. 19 and formulas. Fig. 19 is a diagram showing an example of a filtration blockage simulation in the filtration simulation process of the underwater particle countermeasure support device according to the fourth embodiment.

[0283] The analysis unit D51 uses the following equations (5) to (8) and the calculation means shown in Figure 19 to calculate a predicted value of head loss (m) as a filtration resistance from the measurement results M of underwater particles P such as algae that cause filtration blockage.

[0284]

number

[0285]

number

[0286]

number

[0287]

number

[0288] In the above formulas (5) to (8), "h 0 " indicates the head loss of the clean filter layer (m). "h" indicates the head loss of the clogged filter layer (m). "f" indicates a coefficient. "μ" indicates the viscosity coefficient of the liquid (kg / (m·s)). "v" indicates the filtration velocity (m / s). "L" indicates the thickness of the filter layer (m). "g" indicates the acceleration of gravity (m / s 2 ) and "ρ F " is the density of the liquid (kg / m 3 ) and "Φ" indicates the shape factor of the filter medium. "D" indicates the particle diameter of the filter medium (m). "ε 0 " indicates the initial porosity of the filter media. "K" indicates the filtration resistance coefficient. "X n " indicates the filtration input (cell / L). Also, "α n-1 " indicates explanatory variables.

[0289] The head loss (resistance) of the clean filter layer can be calculated using the Kozeny-Carman equation (5) above.

[0290] The layer with the most blockage has a layer porosity of ε 0 " to "ε 0-σ" and is expressed by the above formula (6). Note that the underwater particle countermeasure support system 100P according to the reference technology cannot determine the filter bed void ratio from the measured value, determine the type of suspended solids, or predict and calculate the filter resistance.

[0291] Here, if the invariants of the above equation (6) and the change in the filter bed void ratio are summarized into the filtration resistance coefficient K(t), it is expressed by the above equation (7).

[0292] As shown in FIG. 19, the analysis unit D51 calculates the filtration resistance coefficient K(t) based on the filtration input X n , filtration velocity V, and head loss h, and the model makes it possible to calculate head loss h. Here, the above formula (8) represents an example of multiple regression analysis.

[0293] In the above filter clogging simulation model, the filter input X n is the calculation result of the above-mentioned coagulation sedimentation simulation, or the actual measurement value of the submerged water W3 sent from the sedimentation basin P5 to the rapid sand filter basin P6. Also, the filtration output (cell / L) does not need to be taken into consideration because its effect is minimal. Also, if the model is nonlinear, it can be a regression model using AI (Artificial Intelligence).

[0294] (Filter clogging prediction graph output processing) The filtration clogging prediction graph output process will be described. The analysis unit D51 executes the filtration clogging prediction graph output process. For example, the analysis unit D51 outputs the analysis result A including the head loss (m), which is the resistance predicted using the measurement result M. The analysis unit D51 also outputs a time series trend graph showing the predicted value of the head loss (m) of the filtered water W4 for each future date and time as the filtration clogging prediction graph A5. The analysis unit D51 can also output the actual measured value of the head loss (m) for each measurement date and time of the filtered water W4 by superimposing it on the time series trend graph of the filtration clogging prediction graph A5. At this time, the analysis unit D51 stores the output time series trend graph of the filtration clogging prediction graph A5 in the analysis result database D52. The analysis unit D51 also transmits the analysis result A including the output head loss (m) to the monitoring control device D1.

[0295] Here, an example of the filtration clogging prediction graph A5 will be described with reference to Fig. 21. Fig. 21 is a diagram showing an example of the filtration clogging prediction graph A5 of the underwater particle countermeasure support device D5 according to embodiment 4. Below, a time series trend graph of the filtration clogging prediction graph A5 will be described.

[0296] An example of a time series trend graph of filtration clogging prediction graph A5 is a time series trend graph of filtration clogging prediction graph A5 which shows, as a line graph, the predicted filtration clogging values ​​(dashed line in Figure 21) and the actual measured filtration clogging values ​​(solid line in Figure 21) of head loss (m), which is the filter resistance, for the 12 days of "April 10th," "April 11th," "April 12th," "April 13th," "April 14th," "April 15th," "April 16th," "April 17th," "April 18th," "April 19th," "April 20th," and "April 21st."

[0297] The examples in Figures 21(1) and (2) show the predicted and measured values ​​when the head loss (m) gradually increases over time and decreases by cleaning the rapid sand filter P6 at regular intervals (e.g., 72 hours).

[0298] The examples in Figures 21 (3) and (4) show the predicted and measured values ​​when the head loss (m) gradually increases over time and reaches the threshold filtration blockage control value of "2.0 m" (dashed line in Figure 21), and then cleaning of the rapid filter basin P6 is carried out to reduce the head loss (m).

[0299] The example in Figure 21 (5) shows the predicted and actual measured values ​​when the head loss (m) gradually increases over time and is predicted to exceed the threshold filtration blockage control value of "2.0 m". By cleaning the rapid filter basin P6 in advance, the head loss (m) decreases before it exceeds the filtration blockage control value.

[0300] (2-2-1-2. Filtration leakage prediction process) The analysis unit D51 executes a filtration leakage prediction process. For example, the analysis unit D51 calculates a predicted value of the filtration output (cell / L) using the measurement result M transmitted from the underwater particle image analysis device D3. The following describes the prediction theory of the filtration leakage prediction process and the filtration leakage prediction graph output process.

[0301] (Prediction theory of filtration leakage prediction process) A prediction theory of a filtration leakage prediction process as a filtration failure prediction process will be described with reference to Fig. 20 and formulas. Fig. 20 is a diagram showing an example of a filtration leakage simulation in the filtration simulation process of the underwater particle countermeasure support device according to the fourth embodiment.

[0302] The analysis unit D51 calculates a predicted value of the filtration output (cell / L) from the measurement results M of underwater particles P such as algae that cause filtration leakage, using the following formula (9) and the calculation means shown in FIG.

[0303]

number

[0304] In the above formula (9), "X n " indicates the filtration input (cell / L). Also, "Yn " indicates the filtration output (cell / L). n-1 " indicates explanatory variables.

[0305] As shown in FIG. 20, the analysis unit D51 performs a filtration leakage model by dividing the filtration input X n and filtration output Y n The above formula (9), which is an example of the relational expression, is obtained.

[0306] In the above modeling of the filtration leakage simulation, the filtration input X n is the calculation result of the coagulation sedimentation simulation described above, or the actual measurement value of the submerged water W3 sent from the sedimentation basin P5 to the rapid sand filter basin P6. The filtration output (cell / L) is the actual measurement value of the filtered water W4 sent from the rapid sand filter basin P6 to the clear water basin P7. If the model is nonlinear, an AI regression model can be used.

[0307] (Filter leakage prediction graph output processing) The filtration leakage prediction graph output process will be described. The analysis unit D51 executes the filtration leakage prediction graph output process. For example, the analysis unit D51 outputs the analysis result A including the filtration output (cell / L) predicted using the measurement result M. The analysis unit D51 also outputs a time series trend graph showing the predicted value of the filtration output (cell / L) of the filtered water W4 for each future date and time as the filtration leakage prediction graph A6. The analysis unit D51 can also output the actual measurement value of the filtration output (cell / L) for each measurement date and time of the filtered water W4 by superimposing it on the time series trend graph of the filtration leakage prediction graph A6. At this time, the analysis unit D51 stores the output time series trend graph of the filtration leakage prediction graph A6 in the analysis result database D52. The analysis unit D51 also transmits the analysis result A including the output filtration output (cell / L) to the monitoring control device D1. Furthermore, the analysis unit D51 outputs, as the filtration leakage prediction graph A6, a time-series trend graph showing the predicted value of the turbidity (mg / L) of the filtered water W4 at each future date and time.

[0308] Here, an example of the filtration leakage prediction graph A6 will be described with reference to Fig. 22. Fig. 22 is a diagram showing an example of the filtration leakage prediction graph A6 of the underwater particle countermeasure support device D5 according to the fourth embodiment. Below, a time-series trend graph of the filtration leakage prediction graph A6, which shows a time-series change in turbidity (mg / L), will be described. At this time, the analysis unit D51 performs a coagulation sedimentation simulation process based on the inflow amount (raw water measurement value) of the raw water W2 as the filtration leakage prediction graph output process, and then performs a filtration simulation process.

[0309] As shown in FIG. 22, an example of a time series trend graph of the filtration leakage prediction graph A6 is a time series trend graph of the filtration leakage prediction graph A6, which is a line graph showing the factor algae raw water inflow value (thick solid line in FIG. 22) indicating the degree of factor algae related to filtration leakage, the factor algae leakage predicted value (dashed line in FIG. 22) indicating the predicted value of filtration leakage converted into turbidity (mg / L), and the factor algae leakage actual value (solid line in FIG. 22) indicating the actual measured value of filtration leakage converted into turbidity (mg / L), for the 12 days of "April 10th", "April 11th", "April 12th", "April 13th", "April 14th", "April 15th", "April 16th", "April 17th", "April 18th", "April 19th", "April 20th", and "April 21st".

[0310] The example in Figure 22 shows the change in turbidity (mg / L) when the inflow of small-particle algae increases over time and then naturally decreases, and shows the predicted and actual values ​​when the turbidity (mg / L) decreases before exceeding the filtration leakage control value by removing underwater particles P from the filtered water W4 in advance when it is predicted that the turbidity will exceed the threshold filtration leakage control value of "0.1 mg / L".

[0311] At this time, if the predicted value of the causative algae leakage exceeds the filtration leakage control value, the operator O will take measures against filtration leakage, for example, by adding the coagulant P11 in two stages (e.g., medium PAC) and implementing underwater particle measures to capture flocs in the rapid filtration basin P6.

[0312] (2-2-1-3. Taste and odor prediction processing) The analysis unit D51 executes the taste and odor prediction process. For example, the analysis unit D51 calculates a predicted value of the odor substance assumed concentration (ng / L), which is the degree of odor generation, using the measurement result M transmitted from the underwater particle image analysis device D3. The prediction theory of the taste and odor prediction process and the taste and odor prediction graph output process are described below.

[0313] (Prediction theory of taste and odor prediction processing) The prediction theory of the taste and odor prediction process as the filtration failure prediction process will be explained using mathematical expressions.

[0314] The analysis section D51 calculates the filtration output Y predicted by the above filtration leakage simulation. n If the sample contains organisms with taste and odor disorders, use formula (3) above to predict the expected concentration of odorous substances (ng / L).

[0315] (Taste and odor prediction graph output processing) The unpleasant odor taste prediction graph output process will be described. The analysis unit D51 executes the unpleasant odor taste prediction graph output process. For example, the analysis unit D51 outputs the analysis result A including the expected odorant concentration (ng / L) predicted using the measurement result M. The analysis unit D51 also outputs a time series trend graph showing the predicted value of the expected odorant concentration (ng / L) for each future date and time of the filtered water W4 as the unpleasant odor taste prediction graph A7. The analysis unit D51 can also output the actual measured value of the expected odorant concentration (ng / L) for each measurement date and time of the filtered water W4 by superimposing it on the time series trend graph of the unpleasant odor taste prediction graph A7. At this time, the analysis unit D51 stores the output time series trend graph of the unpleasant odor taste prediction graph A7 in the analysis result database D52. The analysis unit D51 also transmits the analysis result A including the output expected odorant concentration (ng / L) to the monitoring control device D1.

[0316] Here, an example of the taste and odor prediction graph A7 will be described with reference to Fig. 23. Fig. 23 is a diagram showing an example of the taste and odor prediction graph A7 of the underwater particle countermeasure support device D5 according to the fourth embodiment. Below, a time-series trend graph of the taste and odor prediction graph A7, which shows the time-series change in the concentration (ng / L) of geosmin, an odorant, will be described. At this time, the analysis unit D51 performs a coagulation and sedimentation simulation process based on the inflow amount (raw water measurement value) of the raw water W2 as the taste and odor prediction graph output process, and then performs a filtration simulation process.

[0317] As shown in FIG. 23, an example of a time series trend graph of the taste and odor prediction graph A7 is a time series trend graph of the taste and odor prediction graph A7, which is a line graph showing the factor algae raw water inflow value (thick solid line in FIG. 23) indicating the level of factor algae related to the taste and odor due to filtration leakage, the factor algae leakage predicted value (dashed line in FIG. 23) indicating the predicted value of the taste and odor converted into odor substance concentration (ng / L), and the factor algae leakage actual value (solid line in FIG. 23) indicating the actual measured value of the taste and odor converted into odor substance concentration (ng / L), for the 12 days of "April 10th," "April 11th," "April 12th," "April 13th," "April 14th," "April 15th," "April 16th," "April 17th," "April 18th," "April 19th," "April 20th," and "April 21st."

[0318] The example in Figure 23 shows the change in geosmin (ng / L) when the inflow of small-particle-sized causative algae increases over time and then naturally decreases, and shows the predicted and actual values ​​when geosmin (ng / L) decreases before it exceeds the taste and odor control value by removing underwater particles P from the filtered water W4 in advance when it is predicted that the threshold taste and odor control value of 10 ng / L will be exceeded.

[0319] At this time, if the predicted value of the causative algae leakage exceeds the taste and odor control value, the operator O will implement underwater particle countermeasures as a measure against the taste and odor, for example, by combining the discontinuation of the pretreatment chlorine addition with the two-stage addition of the coagulant P11.

[0320] (2-2-2. Communication section D53) The communication unit D53 manages data communication with other devices. For example, the communication unit D53 performs data communication with each communication device via a router, etc. The communication unit D53 can also perform data communication with an operator's terminal (not shown).

[0321] (2-3. Example of the configuration and processing of display terminal D6) A configuration example and a processing example of the display terminal D6 will be described with reference to Fig. 6 again. The display terminal D6 is a terminal used by an operator O who is a manager who manages the sample water W, and is a manager terminal having an input / output unit D61, a control unit D62, and a communication unit D63.

[0322] (2-3-1. Input / output section D61) The input / output unit D61 controls input of various information to the display terminal D6. For example, the input / output unit D61 is realized by a mouse, a keyboard, a touch panel, etc., and accepts input of setting information, etc. to the display terminal D6. The input / output unit D61 also controls display of various information from the display terminal D6. For example, the input / output unit D61 is realized by a display, etc., and displays setting information, etc. stored in the display terminal D6.

[0323] Furthermore, the input / output unit D61 displays the analysis results A transmitted from the underwater particle countermeasure support device D5. For example, the input / output unit D61 displays, as the analysis results A, a filtration blockage prediction graph A5, a filtration leakage prediction graph A6, an unpleasant taste and odor prediction graph A7, and the like.

[0324] (2-3-2. Control unit D62) The control unit D62 transmits various information. The control unit D62 also receives various information. For example, the control unit D62 receives the analysis result A from the underwater particle countermeasure support device D5.

[0325] (2-3-3. Communication section D63) The communication unit D63 manages data communication with other devices. For example, the communication unit D63 performs data communication with each communication device via a router, etc. The communication unit D63 can also perform data communication with an operator's terminal (not shown).

[0326] 3. Effects of the Fourth Embodiment Finally, a description will be given of the effects of the fourth embodiment. Below, effects 1 to 6 corresponding to the processing according to the fourth embodiment will be described.

[0327] (3-1. Effect 1) First, in the process according to the above-mentioned fourth embodiment, the underwater particle countermeasure support device D5 outputs the analysis result A including the head loss (m) predicted using the measurement result M. Therefore, in this process, by outputting the analysis result A predicting the occurrence of filtration blockage, it is possible to efficiently implement countermeasures against filtration blockage, which is one of the problems caused by underwater particles P.

[0328] (3-2. Effect 2) Secondly, in the process according to the above-mentioned embodiment 4, the underwater particle countermeasure support device D5 outputs the analysis result A including the filtration output (cell / L) predicted using the measurement result M. Therefore, in this process, by outputting the analysis result A predicting the occurrence of filtration leakage, it is possible to efficiently implement countermeasures against filtration leakage, one of the problems caused by underwater particles P.

[0329] (3-3. Effect 3) Thirdly, in the process according to the above-mentioned embodiment 4, the underwater particle countermeasure support device D5 outputs the analysis result A including the assumed odorant concentration (ng / L) predicted using the measurement result M. Therefore, in this process, by outputting the analysis result A predicting the occurrence of off-odor tastes, it is possible to efficiently implement countermeasures against off-odor tastes, which are among the problems caused by underwater particles P.

[0330] (3-4. Effect 4) Fourthly, in the process according to the above-mentioned embodiment 4, the monitoring control device D1 issues an alarm predicting the occurrence of filtration blockage when the head loss (m) indicated by the analysis result A exceeds the filtration blockage control value. Therefore, in this process, by automatically notifying the operator O of the filtration blockage prediction, measures against filtration blockage, which is one of the problems caused by underwater particles P, can be efficiently implemented.

[0331] (3-5. Effect 5) Fifth, in the process according to the above-mentioned embodiment 4, the monitoring control device D1 issues an alarm predicting the occurrence of a filtration leakage when the filtration output (cell / L) indicated by the analysis result A exceeds the filtration leakage management value. Therefore, in this process, by automatically notifying the operator O of the filtration leakage prediction, measures against the filtration leakage, which is one of the problems caused by the underwater particles P, can be efficiently implemented.

[0332] (3-6. Effect 6) Sixth, in the process according to the above-mentioned embodiment 4, the monitoring control device D1 issues an alarm predicting the occurrence of an unpleasant taste or odor when the odor substance expected concentration (ng / L) indicated by the analysis result A exceeds the unpleasant taste or odor control value. Therefore, in this process, by automatically notifying the operator O of the unpleasant taste or odor prediction, measures against the unpleasant taste or odor, which is one of the problems caused by the underwater particles P, can be efficiently implemented.

[0333] 〔others〕 Some examples of combinations of the disclosed technical features are set out below.

[0334] (1) A countermeasure support system comprising an image analysis device and a countermeasure support device, wherein the image analysis device comprises an imaging unit that photographs collected test water and acquires image data of the test water, and a measurement unit that measures underwater particles contained in the test water based on the image data of the test water, and the countermeasure support device comprises an analysis unit that outputs analysis results to be used for countermeasures against problems caused by the underwater particles based on the measurement results of the underwater particles transmitted from the image analysis device.

[0335] (2) The countermeasure support system described in (1), further comprising a server device that identifies underwater particles contained in the measurement water based on image data of the measurement water transmitted from the image analysis device, transmits an image library including image data of the identified underwater particles to the image analysis device, and transmits characteristics of the identified underwater particles to the countermeasure support device.

[0336] (3) The countermeasure support system according to (1) or (2), further comprising a sampling device that samples a plurality of sample waters and supplies each of the sampled plurality of sample waters to the image analysis device.

[0337] (4) A countermeasure support system described in any one of (1) to (3), wherein the measurement unit of the image analysis device classifies the underwater particles contained in the image data of the measurement water using an image library containing image data of the underwater particles, and measures the number of particles for each classified underwater particle.

[0338] (5) A countermeasure support system described in any one of (1) to (4), wherein the measurement unit of the image analysis device calculates features of the underwater particles by analyzing image data of the measured water, and measures the number of particles for each of the underwater particles having similar features.

[0339] (6) A countermeasure support system described in any one of (1) to (5), wherein the analysis unit of the countermeasure support device outputs the analysis result including at least one of the measurement results, characteristics of the underwater particles identified using image data of the measured water, and types of obstacles based on the characteristics.

[0340] (7) A countermeasure support system described in any one of (1) to (6), wherein the analysis unit of the countermeasure support device outputs the analysis results including at least one of a graph showing the measurement results and a graph showing the degree of occurrence of a problem based on the characteristics of the underwater particles for each of the multiple measurement waters collected at each of the multiple measurement positions.

[0341] (8) A countermeasure support system described in any one of (1) to (7), wherein the analysis unit of the countermeasure support device outputs the analysis results including at least one of a graph showing the measurement results and a graph showing the degree of occurrence of a problem based on the characteristics of the underwater particles for each of the multiple measurement waters collected at each of the multiple measurement times.

[0342] (9) A countermeasure support system described in any one of (1) to (8), wherein the analysis unit of the countermeasure support device outputs the analysis result including a graph showing the degree of odor generation calculated using the measurement results for each of multiple test waters collected at multiple measurement positions.

[0343] (10) A countermeasure support system described in any one of (1) to (9), wherein the analysis unit of the countermeasure support device outputs the analysis result including a graph showing the degree of odor generation calculated using the measurement results for each of multiple test waters collected at multiple measurement times.

[0344] (11) The countermeasure support system according to any one of (1) to (10), wherein the analysis unit of the countermeasure support device outputs the analysis result including an injection rate of powdered activated carbon calculated based on the measurement result.

[0345] (12) A countermeasure support system according to any one of (1) to (11), wherein the analysis unit of the countermeasure support device outputs the analysis result including an injection rate of a flocculant calculated using the measurement result.

[0346] (13) A countermeasure support system according to any one of (1) to (12), wherein the analysis unit of the countermeasure support device outputs the analysis result including an injection rate of a pH adjuster calculated using the measurement result.

[0347] (14) The countermeasure support system according to any one of (1) to (13), wherein the analysis unit of the countermeasure support device outputs the analysis result including a resistance predicted using the measurement result.

[0348] (15) The countermeasure support system according to any one of (1) to (14), wherein the analysis unit of the countermeasure support device outputs the analysis result including a filtration output predicted using the measurement result.

[0349] (16) The countermeasure support system according to (15), wherein the analysis unit of the countermeasure support device outputs the analysis result including a predicted degree of odor generation using the measurement result.

[0350] (17) The countermeasure support system according to (11), further comprising a monitoring countermeasure device that controls the injection of the powdered activated carbon into the sample water in accordance with an injection rate of the powdered activated carbon indicated by the analysis result.

[0351] (18) The countermeasure support system according to (12), further comprising a monitoring countermeasure device that controls the injection of the coagulant into the test water according to the injection rate of the coagulant indicated by the analysis result.

[0352] (19) The countermeasure support system according to (13), further comprising a monitoring countermeasure device that controls injection of the pH adjuster into the sample water in accordance with an injection rate of the pH adjuster indicated by the analysis result.

[0353] (20) The countermeasure support system according to (14), further comprising a monitoring countermeasure device that issues an alarm predicting the occurrence of filtration blockage when the resistance indicated by the analysis result exceeds a threshold value.

[0354] (21) The countermeasure support system described in (15), further comprising a monitoring countermeasure device that issues an alarm predicting the occurrence of a filtration leak when the filtration output indicated by the analysis result exceeds a threshold value.

[0355] (22) The countermeasure support system described in (16), further comprising a monitoring and countermeasure device that issues an alarm predicting the occurrence of an abnormal odor or taste if the degree of odor occurrence indicated by the analysis result exceeds a threshold value.

[0356] (23) A countermeasure support system according to any one of (1) to (22), further comprising an administrator terminal used by an administrator who manages the measurement water, the administrator terminal displaying the analysis results transmitted from the countermeasure support device.

[0357] (24) The countermeasure support system according to any one of (1) to (23), wherein the test water is collected at each stage of a water purification treatment for a water supply system.

[0358] (25) The countermeasure support system according to any one of (1) to (24), wherein the underwater particles are algae contained in the measurement water.

[0359] (26) A countermeasure support method in a countermeasure support system including an image analysis device and a countermeasure support device, the image analysis device photographing collected test water, acquiring image data of the test water, and measuring underwater particles contained in the test water based on the image data of the test water; and the countermeasure support device outputting analysis results to be used for countermeasures against problems caused by the underwater particles based on the measurement results of the underwater particles transmitted from the image analysis device. [Explanation of symbols]

[0360] D1 Monitoring and control device D11 Executive Department D12 Communications Department D2 Sampling Device D21 Pump D22 Defoaming tank D3 Underwater particle image analyzer D31 Photography Department D31a Cell D31b light source D31c Camera D31d Pump D32 Measuring part D33 Communications Department D34 Classification Model D4 Underwater particle data server D41 Analysis section D42 Underwater Particle Database D5 Underwater particle countermeasure support device D51 Analysis Department D52 Analysis Results Database D53 Communications Department D6 Display terminal D61 Input / output section D62 Control section D63 Communications Department 100-1, 100-2, 100-3, 100-4 Underwater particle countermeasure support system

Claims

1. A countermeasure support system comprising at least one device, The at least one of the devices is A process of taking a photograph of the water to be measured and obtaining image data of the water to be measured, Based on the aforementioned image data, the aquatic particles contained in the measured water are classified by type using a machine learning model trained with training data, and the number of particles of each type classified in the aquatic particles is measured. A process for calculating an index indicating the occurrence of water treatment problems based on the number of particles of each type in the water and the characteristics of the water particles associated with each type of water particle, A process to output analysis results to be used for countermeasures based on the aforementioned indicators, A support system for implementing countermeasures.

2. Further comprising a server device other than the at least one device, The server device is Based on the aforementioned image data, the underwater particles are identified, An image library containing image data of the identified underwater particles is generated, The image library is transmitted to the at least one device. The characteristics of the identified water particles are transmitted to the at least one device. The countermeasure support system according to claim 1.

3. The system further includes a sampling device that collects multiple water samples and supplies each of the collected water samples to at least one of the devices. The countermeasure support system according to claim 1.

4. The machine learning model is a model trained using an image library containing image data of the identified underwater particles. The countermeasure support system according to claim 1.

5. The process for classifying the particles in the water is: The feature quantities calculated from the aforementioned image data are classified. The countermeasure support system according to claim 1.

6. The analysis results include at least one of the characteristics of the water particles and the type of obstruction associated with those characteristics, The countermeasure support system according to claim 1.

7. The at least one device is For each of the multiple water samples collected at each of the multiple measurement locations, the analysis results are output, which include at least one of the following: a graph showing the measurement results and a graph showing the degree of damage based on the characteristics of the water particles. The countermeasure support system according to claim 1.

8. The at least one device is For each of the multiple water samples collected at each of the multiple measurement locations, the analysis results are output, including a graph showing the degree of odor generation calculated using the measurement results. The countermeasure support system according to claim 1.

9. The analysis results include the injection rate of powdered activated carbon determined based on the number of particles of each type in the water and the index indicating the occurrence state of water treatment problems calculated based on the characteristics of the particles in the water, The countermeasure support system according to claim 1.

10. The analysis results include the injection rate of a coagulant determined based on the number of particles of each type in the water and the index indicating the occurrence state of water treatment problems calculated based on the characteristics of the particles in the water, The countermeasure support system according to claim 1.

11. The analysis results include the injection rate of a pH adjuster determined based on the number of particles of each type in the water and the index indicating the occurrence state of water treatment problems calculated based on the characteristics of the particles in the water, The countermeasure support system according to claim 1.

12. The analysis results include a predicted filter resistance based on the number of particles of each type in the water and the time-series change of the index indicating the occurrence state of water treatment problems, calculated based on the characteristics of the particles in the water, The countermeasure support system according to claim 1.

13. The analysis results include the number of particles of each type in the water and the predicted filtration output based on the time-series change of the index indicating the occurrence state of water treatment problems, which is calculated based on the characteristics of the particles in the water, The countermeasure support system according to claim 1.

14. The analysis results include the number of particles of each type in the water and the predicted degree of odor generation based on the time-series change of the index indicating the occurrence state of water treatment problems, calculated based on the characteristics of the water particles. The countermeasure support system according to claim 1.

15. A monitoring and control device further comprising a device that controls the injection of powdered activated carbon into the measurement water according to the injection rate of powdered activated carbon calculated based on the index, The countermeasure support system according to claim 9.

16. A monitoring and control device further comprising a device that controls the injection of the coagulant into the measured water according to the injection rate of the coagulant calculated based on the index, The countermeasure support system according to claim 10.

17. A monitoring and control device further comprising a device that controls the injection of the pH adjusting agent into the measurement water according to the injection rate of the pH adjusting agent calculated based on the index, The countermeasure support system according to claim 11.

18. A monitoring control device further comprising a device that notifies an alarm indicating the occurrence of filtration blockage when the predicted filtration resistance based on the indicator exceeds a predetermined threshold, The countermeasure support system according to claim 12.

19. A monitoring and control device that further provides an alarm indicating the occurrence of a filtration leak when the filtration output predicted based on the indicator exceeds a predetermined threshold, The countermeasure support system according to claim 13.

20. A monitoring control device that further provides an alarm indicating the occurrence of an off-flavor when the degree of odor generation predicted based on the indicator exceeds a predetermined threshold, The countermeasure support system according to claim 14.

21. Further comprising an administrator terminal that displays the analysis results output from at least one of the devices, The analysis results include the indicators that show the occurrence of water treatment problems or countermeasures based on the indicators, The countermeasure support system according to claim 1.

22. The aforementioned water samples are collected at each stage of the water purification treatment process for the tap water supply. The at least one of the devices is Based on the number of particles of each type of underwater particle corresponding to each of the above processes and the characteristics of the underwater particles, the index is calculated. The countermeasure support system according to claim 1.

23. The aforementioned aquatic particles include algae, The aforementioned underwater particle characteristics include odor substance generation characteristics or aggregation characteristics corresponding to the type of algae, The countermeasure support system according to claim 1.

24. A step of taking a photograph of the water to be measured and obtaining image data of the water to be measured, Based on the aforementioned image data, the water particles contained in the measured water are classified by type using a machine learning model trained with training data, and the number of particles of each type classified in the water is measured. A step of calculating an index indicating the occurrence of water treatment problems based on the number of particles of each type in the water and the characteristics of the water particles associated with each type of water particle, A step of outputting analysis results to be used for countermeasures based on the aforementioned indicators, Methods of support for countermeasures, including those mentioned above.

25. A computer, To photograph the water being measured and obtain image data of the water being measured, Based on the aforementioned image data, the water particles contained in the measured water are classified by type using a machine learning model trained with training data, and the number of particles of each type classified in the water is measured. Based on the number of particles of each type in the water and the characteristics of the water particles associated with each type of water particle, an index indicating the occurrence of water treatment problems is calculated. Outputting analysis results to be used for countermeasures based on the aforementioned indicators, A support program to implement countermeasures.

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