JP2017106807A - Foul smell estimation device, foul smell estimation system, foul smell estimation method, and foul smell estimation program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a foul smell estimation device, a foul smell estimation system, a foul smell estimation method, and a foul smell estimation program with which it is possible to shorten the time needed for detection of a foul smell in raw water.SOLUTION: A foul smell estimation of an embodiment of the present invention includes an image input unit, an algae amount acquisition unit, and a foul smell estimation unit. The image input unit accepts image data the subject of which is raw water that is inputted from an imaging unit that converts light from the subject to an electrical signal for each of a plurality of wavelength bands. The algae amount acquisition unit extracts the pixel area of a first algae included in the raw water on the basis of the signal strengths of the plurality of wavelength bands in the image data inputted to the image input unit, and acquires the amount of the first algae per unit quantity of raw water on the basis of the extracted pixel area of the first algae. The foul smell estimation unit estimates the level of foul smell of the raw water on the basis of the amount of the first algae acquired by the algae amount acquisition unit.SELECTED DRAWING: Figure 4

Description

  Embodiments described herein relate generally to a strange odor estimation apparatus, a strange odor estimation system, a strange odor estimation method, and a strange odor estimation program.

  In recent years, with the advance of water purification treatment technology, the off-flavor damage such as musty odor of tap water has been decreasing. However, although it is a small number, the off-flavor damage is still occurring. Water supply companies have introduced advanced water purification treatment and powdered activated carbon as measures against musty odor. The powdered activated carbon is introduced by empirically predicting musty odor generation according to the season and climate. As described above, in the prediction based on experience, when powdered activated carbon is introduced, it is necessary to add considerably more powdered activated carbon in consideration of the low accuracy of the prediction. That is, there is a problem that powdered activated carbon needs to be added more than necessary, and powdered activated carbon cannot be used efficiently.

  In order to efficiently use the powdered activated carbon, it is conceivable to detect the generation of musty odor and control the charging of the powdered activated carbon according to the generation of the musty odor. As a method for detecting the generation of musty odor, there is a musty odor detection device using gas chromatography mass spectrometry (GCMS method) that directly measures the musty odor substances geosmin and 2-methylisoborneol (2-MIB) from raw water. Exists. However, such musty odor detectors are very expensive and have not been installed in water purification plants and require a lot of time for one measurement. If one measurement takes time, for example, there is a problem that it cannot be handled when the musty odor suddenly becomes strong.

  As described above, the conventional technique has a problem that it takes a long time to detect a bad odor such as musty odor in raw water.

Japanese Patent Application Laid-Open No. 09-75918

  The problem to be solved by the present invention is to provide an odor estimation apparatus, an odor estimation system, an odor estimation method, and an odor estimation program that can shorten the time required to detect an odor in raw water.

  The off-flavor estimation apparatus of the embodiment includes an image input unit, an algae amount acquisition unit, and a off-flavor estimation unit. The image input unit receives image data of raw water as a subject from an imaging unit that photoelectrically converts light from the subject for each of a plurality of wavelength bands. The algae amount acquisition unit extracts the pixel region of the first algae contained in the raw water based on the signal intensities of the plurality of wavelength bands in the image data input to the image input unit, and extracts the extracted first algae pixels Based on the region, the amount of the first algae per unit amount of raw water is acquired. The off-flavor estimation unit estimates the degree of off-flavor of raw water based on the amount of the first algae acquired by the algae amount acquisition unit.

The figure which shows the structural example of the musty smell estimation system 1 in this embodiment. The figure which shows the example of a photograph of the algae which generate | occur | produces in raw | natural water. The figure which shows the spectral spectrum of "diatom", "microkistis", "anabena", "osillatria", and "formidium". The figure which shows the structural example of the musty smell estimation apparatus 2 of 1st Embodiment. The flowchart explaining operation | movement of the musty smell estimation apparatus 2 of 1st Embodiment. The figure which shows the structural example of the musty smell estimation apparatus 2A of 2nd Embodiment. The flowchart explaining operation | movement of the musty odor estimation apparatus 2A of 2nd Embodiment. The figure which shows the image processing example with respect to the image data image | photographed with the imaging part 15 which is a hyper spectrum camera.

  Hereinafter, an odor estimation apparatus, an odor estimation system, an odor estimation method, and an odor estimation program according to embodiments will be described with reference to the drawings.

(Outline)
First, an outline of a musty odor estimation system (a strange odor estimation system) in the present embodiment will be described.
FIG. 1 is a diagram showing a configuration example of a musty odor estimation system 1 in the present embodiment. As shown in FIG. 1, the musty odor estimation system 1 includes a musty odor estimation device 2, a display device 3, an input device 4, a raw water photographing device 5, and a cable 6.

  The raw water imaging device 5 includes a sampling port 11, a sampling tube 12, a shooting tray 13, a light shielding wall 14, an imaging unit 15, an optical system 16, a light source 17, a drain pipe 18, and a drain port 19. Is provided. The water sampling port 11 is an inflow port for sampling a part of raw water taken from a water source. The raw water flowing from the sampling port 11 is subjected to water pressure by a height difference or a pump, and the raw water flows in the direction of the arrow. The water sampling pipe 12 is a pipe through which raw water flowing from the water sampling port 11 flows.

  The photographing tray 13 is a tray in which raw water that has passed through the water sampling pipe 12 is accumulated. The imaging tray 13 is also a tray that accumulates raw water to be imaged by the imaging unit 15. For example, a white member is arranged on the imaging tray 13 so that light from the light source 17 is uniformly reflected regardless of the wavelength, at least in a region to be imaged by the imaging unit 15 if the raw water is transparent. Yes.

  The light shielding wall 14 is, for example, a box shape having an upper surface on which the imaging unit 15 and the light source 17 are installed and a side surface that supports the upper surface. The light shielding wall 14 has a function of shielding the raw water on the photographing tray 13 photographed by the imaging unit 15 from light (external light or the like) other than the light from the light source 17. For example, the inner wall surface of the light shielding wall 14 is coated with an antireflection film to suppress the influence of flare, stray light, and the like during imaging.

  The imaging unit 15 includes an imaging device in which pixels having sensitivity with which a peak of signal intensity in a plurality of specific wavelength bands can be distinguished are arranged. The imaging device provided in the imaging unit 15 has a configuration in which pixels that photoelectrically convert light from a subject are arranged, and may be a single plate or a multi-plate. The specific wavelength is a plurality of wavelength bands that are effective for specifying a cyanobacteria that generates musty odors, for example, 650 nm to 700 nm (first wavelength band), and 600 nm to 650 nm (second wavelength band). It is. In addition, a pixel having sensitivity capable of distinguishing signal intensity peaks in a plurality of specific wavelength bands refers to a plurality of wavelengths capable of determining that there are signal intensity peaks in the first wavelength band and the second wavelength band. This is a pixel having sensitivity in the band. The plurality of wavelength bands capable of determining that there is a spectrum peak between the first wavelength band and the second wavelength band are, for example, wavelengths of 550 nm to 600 nm, 600 nm to 650 nm, 650 nm to 700 nm, and 700 nm to 750 nm. It is a band. The imaging unit 15 processes signals from pixels having sensitivity in these multiple wavelength bands and outputs them as raw water image data. Accordingly, the musty odor estimation apparatus 2 has spectrum peaks at 650 nm to 700 nm (first wavelength band) and 600 nm to 650 nm (second wavelength band) based on the raw water image data from the imaging unit 15. It can be determined whether or not.

  The optical system 16 is a lens for focusing on the raw water on the photographing tray 13. In addition, when photographing a photographing object at a high magnification, the optical system 16 may be configured by a microscope. The light source 17 is an illuminating device that irradiates, for example, white light onto a subject to be imaged. The imaging unit 15 outputs, to the musty odor estimation apparatus 2, raw water image data that is image data including spectral data of a specific wavelength in each pixel obtained by photographing the raw water on the photographing tray 13 via the cable 6. To do.

  The drain pipe 18 is a pipe through which the raw water overflowing from the photographing tray 13 flows to the drain port 19. The drain port 19 is an outlet for draining raw water to the outside. Note that the position of the light source 17 may be positioned facing the imaging unit 15 and below the shooting tray 13. In this configuration, the shooting tray 13 is transparent so that the light from the light source 17 can be transmitted.

  The musty odor estimation device 2 is a computer that is connected to the imaging unit 15 of the raw water imaging device 5 via the cable 6 and has a function of acquiring raw water image data from the imaging unit 15. Moreover, the musty odor estimation apparatus 2 has a function of outputting musty odor information, which is information obtained by estimating the degree of musty odor of raw water based on the acquired raw water image data. Moreover, the musty odor estimation apparatus 2 has a function of classifying the raw water image data for each pixel region having similar spectrum data and performing color coding.

  The display device 3 is a display device connected to the musty odor estimating device 2 which is a computer, and displays musty odor information. The display device 3 may display the raw water image data and the analysis result of the raw water image data as necessary. The input device 4 is an input device such as a keyboard and a mouse connected to the musty odor estimation device 2 which is a computer.

  The input device 4 may be a device that can attach and detach a recording medium connected to the musty odor estimation device 2 that is a computer and can read data from the attached recording medium. Thereby, the input device 4 can input the data recorded on the recording medium. In addition, it is good also as a structure which integrated the musty-odor estimation apparatus 2, the display apparatus 3, and the input device 4 using the touchscreen terminal.

  Here, a specific example of the method for selecting a specific wavelength band will be described. Various types of cyanobacteria that produce musty odors by shooting a specific wavelength band of various algae with a hyperspectral camera capable of acquiring signal intensity of each wavelength in increments of 5 to 10 nm at wavelengths of 400 nm to 1000 nm with each pixel Identify the spectroscopic spectra of algae. Then, if there is a wavelength band in which the values of the spectral spectrum are remarkably different between cyanobacteria that generate musty odor and other algae, the wavelength band may be set as a specific wavelength band. Further, if there are a plurality of peak wavelengths peculiar to cyanobacteria that generate musty odor, the pixel region of cyanobacteria that generates musty odor may be specified using values of a plurality of wavelength bands each including the plurality of peak wavelengths. .

  Examples of cyanobacteria that cause musty odor include “Anabaena”, “Oshiratoria”, and “formidium”, and cyanobacteria that do not cause musty odor include “microkistis”. FIG. 2 is a diagram showing an example of a photograph of algae generated in raw water. In FIG. 2, a photograph 41 is “Oshiratoria” which is a cyanobacteria that generates musty odor. Photo 42 is “Anavena”, a cyanobacteria that generates musty odor. Photo 43 is “Formidium”, a cyanobacteria that generates musty odor. Photo 44 is “diatom” which is one of the algae other than cyanobacteria that does not generate musty odor. Photo 45 is “Microkistis”, a cyanobacteria that does not generate musty odor.

  As shown in FIG. 2, “Anavena”, “Oshiratoria” and “Formidium”, which are cyanobacteria that generate musty odor, have a rod-like shape. “Microkistis”, which is a cyanobacteria that does not generate musty odor, and “diatom”, which is one of the algae other than cyanobacteria that does not generate musty odor, have a spherical shape.

  FIG. 3 is a diagram showing spectral spectra of “diatoms”, “Microkistis”, “Anabaena”, “Oshiratoria” and “Formidium”. As shown in FIG. 3, the microalgae “Microkistis”, “Anabaena”, “Oshiratria”, and “Formidium” are around 620 nm (frame 51 in FIG. 3) and 680 nm (frame 52 in FIG. 3). There is a spectral spectrum peak (downward peak in FIG. 3). The “diatom” has spectral spectrum peaks (downward in FIG. 3) at around 500 nm and around 680 nm. FIG. 3 shows a frame 51 indicating a wavelength band near 620 nm and a frame 52 indicating a wavelength band near 680 nm. The first wavelength band includes the wavelength band of the frame 52, and the second wavelength band includes the wavelength band of the frame 51.

  It is considered that the peak near 620 nm appears mainly due to light absorption by a pigment called phycocyanin. It is considered that the peak around 680 nm appears mainly due to light absorption by a pigment called chlorophyll a. Thus, the wavelength band corresponding to the pigment contained in the algae is the peak of the spectral spectrum. Therefore, if there is a pigment peculiar to algae that generates a strange odor such as musty odor, the wavelength band of light absorbed by the pigment becomes a wavelength band peculiar to the algae. There are few phycocyanins in other algae other than cyanobacteria, and phycocyanin can be said to be a pigment unique to cyanobacteria.

  Considering the characteristics of the above algae, first, in the second wavelength band including the vicinity of 620 nm and the first wavelength band including the vicinity of 680 nm, which are the wavelength bands of the peaks specific to cyanobacteria output from the imaging unit 15. The signal intensity is used to specify whether or not the pixel area is cyanobacteria. Next, it is considered that image processing is performed on the raw water image data output from the imaging unit 15, and the cyanobacterial pixel region that generates musty odor can be identified in accordance with the cyanobacterial pixel region and the shape of the identified algae. The musty odor estimation apparatus 2 according to the present embodiment specifies a cyanobacterial pixel region based on the wavelength band based on the raw water image data output from the imaging unit 15 and then uses a rod-like shape based on the shape of the cyanobacterial pixel region. A process of discriminating the pixel region of cyanobacteria from the pixel region of cyanobacteria that generates musty odor is performed.

Next, the flow of musty odor estimation processing in the musty odor estimation system 1 will be described.
For example, when raw water is introduced into a pipe connected to the water sampling port 11 at a predetermined water pressure, the raw water is accumulated in the photographing tray 13 through the water sampling port 11 and the water sampling pipe 12. The imaging unit 15 photographs the raw water accumulated in the photographing tray 13 and outputs the raw water image data to the musty odor estimation device 2 via the cable 6. The musty odor estimation device 2 calculates musty odor information indicating the degree of musty odor based on the received raw water image data, and causes the display device 3 to display the calculated musty odor information.

(First embodiment)
Next, a configuration example of the musty odor estimation apparatus 2 in the first embodiment will be described.
FIG. 4 is a diagram illustrating a configuration example of the musty odor estimation apparatus 2 according to the first embodiment. As shown in FIG. 4, the musty odor estimation apparatus 2 includes an image input unit 21, a wavelength band processing unit 22, a shape determination processing unit 23, an algae amount acquisition unit 24, a musty odor estimation table 25, and an input processing unit 26. , A musty odor estimation unit 27 and a display control unit 28.

  The cable 6 is connected to the image input unit 21, and raw water image data photographed from the imaging unit 15 in the raw water photographing device 5 is input. The wavelength band processing unit 22 extracts a cyanobacteria pixel region from the raw water image data based on the signal intensity of the first wavelength band and the second wavelength band included in the raw water image data input to the image input unit 21. To do. For example, the wavelength band processing unit 22 identifies pixels having peaks in the first wavelength band and the second wavelength band, and extracts the identified pixel region as a pixel region of cyanobacteria.

  For example, when the signal intensity of the first and second wavelength bands is lower than the signal intensity of the wavelength bands before and after the first and second wavelength bands, the wavelength band processing unit 22 It is determined that there is a peak in the wavelength band. Here, the front and rear wavelength bands are a wavelength band having a shorter wavelength than the first and second wavelength bands and a wavelength band having a longer wavelength than the first and second wavelength bands. In some cases, for example, the wavelength band processing unit 22 determines the first wavelength when the signal intensity of the first wavelength band is lower than the signal intensity of the wavelength band before or after the first wavelength band. It may be determined that the band has a peak. The above peak determination method is an example, and the wavelength band processing unit 22 determines the peak in the first wavelength band and the second wavelength band for each pixel based on the change in signal intensity for each wavelength band. Judgment is made.

Further, the extraction process of the cyanobacteria pixel region in the wavelength band processing unit 22 is not limited to the process of extracting based on the presence or absence of peaks in the first wavelength band and the second wavelength band. The extraction process shown in FIG. For example, the wavelength band processing unit 22 calculates a cyanobacteria index using the following (Equation 1) based on the pixel data A in the first wavelength band and the pixel data B in the second wavelength band.
Cyanobacteria index = (mA−nB) / (mA + nB) (Formula 1)

  However, m and n are arbitrary coefficients, for the purpose of improving the accuracy of the cyanobacteria index, for example, coefficients that take values from −1 to 1 (−1 ≦ m ≦ 1, −1 ≦ n ≦ 1). ). Coefficients m and n are the values for cyanobacteria and other algae based on the results of changing the values of coefficients m and n by inputting the values of A and B actually measured for multiple types of algae. Find a value that can be determined accurately. In this case, the wavelength band processing unit 22 determines whether or not the pixel region of cyanobacteria is based on the calculated cyanobacteria index, and extracts the pixel region of cyanobacteria.

  The shape discrimination processing unit 23 discriminates the shape of the pixel region of cyanobacteria extracted by the wavelength band processing unit 22, and is a rod-like cyanobacteria that is a cyanobacteria that generates musty odor (Oshiratoria, Anabena, Formidium in FIG. 2). Are extracted. Specifically, it is determined whether it is the rod-shaped cyanobacteria shown in Photos 41 to 43 in FIG. 2 or the spherical cyanobacteria shown in Photo 45. The discrimination method in the shape discrimination processing unit 23 can be realized by using known image processing such as pattern matching using a rod-shaped template and a spherical template.

  The algae amount acquisition unit 24 counts the number of pixels in the pixel area of the rod-like cyanobacteria (= the cyanobacteria that generate musty odor) extracted by the shape discrimination processing unit 23 and generates a musty odor per unit amount of raw water. Obtain information on the amount of cyanobacteria. For example, the algae amount acquisition unit 24 is a target for capturing raw water image data in consideration of the depth of the imaging tray 13 and the imaging range of the imaging unit 15 (or the area that is the imaging range of the imaging tray 13). The amount of cyanobacteria per unit amount of raw water is calculated by specifying the volume of raw water.

  The musty odor estimation table 25 is a table in which the amount of cyanobacteria that generate musty odor per unit amount of raw water is associated with the degree of musty odor. The musty odor estimation table 25 is, for example, a table in which the degree of musty odor increases as the amount of cyanobacteria per unit amount of raw water increases. The method of creating this table is, for example, by changing the amount of cyanobacteria per unit amount of water and determining the degree of each musty odor and classifying it into a plurality of ranks. Create a table that correlates rank indicating the degree of musty odor. Here, the rank indicating the degree of musty odor corresponds to, for example, a unit of the amount of powdered activated carbon input to the raw water. Thus, the musty odor estimation device 2 informs the manager of the water purification plant of the rank of the musty odor, so that the manager of the water purification plant can easily grasp the optimum amount of powdered activated carbon.

  The method of creating the table is not limited to this, and the table may be classified into a plurality of ranks by measuring the degree of musty odor using gas chromatography mass spectrometry (GCMS method) instead of a person. In addition to expressing the degree of musty odor by rank, if there is a measurement value measured by gas chromatography mass spectrometry, a table in which the measurement value is associated with the amount of cyanobacteria may be used. .

  In addition, the musty odor estimation table 25 includes the amount of cyanobacteria that generates musty odor per unit amount of raw water, as well as the temperature of the raw water, the pH value, and the climate of the raw water at the time of sampling. Information determined to be affected may be stored. The method of preparing the table when it is determined that the water temperature affects the degree of musty odor is, for example, by changing the amount of cyanobacteria per unit amount of water, and also changing the water temperature in the amount of each cyanobacteria, A person judges the degree of musty odor in the case and classifies it into a plurality of ranks, thereby creating a table in which the amount of cyanobacteria, the water temperature, and the rank indicating the degree of musty odor are associated.

  The input processing unit 26 receives input of data and the like from the input device 4. The input processing unit 26 records the table itself stored in the musty odor estimation table 25 input from the input device 4 and the data in the table in the musty odor estimation table 25.

  The musty odor estimation unit 27 refers to the musty odor estimation table 25 and estimates the degree of musty odor based on the amount of cyanobacteria that generates the musty odor acquired by the algae amount acquisition unit 24. The musty odor estimation unit 27 estimates the degree of musty odor according to the amount of cyanobacteria that generates musty odor by specifying the degree of musty odor corresponding to the amount of cyanobacteria that generates musty odor from the musty odor estimation table 25. The musty odor estimation table 25 outputs, for example, rank information as the degree of musty odor to the display control unit 28. The display control unit 28 causes the display device 3 to display a rank indicating the degree of musty odor estimated by the musty odor estimating unit 27.

  Although not shown in FIG. 4, the display control unit 28 may be configured to hold an activated carbon input amount table that is a table of input amounts of powdered activated carbon corresponding to the rank indicating the degree of musty odor. Thereby, the display control part 28 can display the input amount of the powdered activated carbon according to the said rank with the rank which shows the grade of musty odor on the display apparatus 3. FIG.

Next, the operation of the musty odor estimation apparatus 2 in the first embodiment will be described.
FIG. 5 is a flowchart for explaining the operation of the musty odor estimation apparatus 2 of the first embodiment. The image input unit 21 receives the raw water image data taken from the imaging unit 15 (step S101). The wavelength band processing unit 22 identifies pixels having peaks in the first wavelength band and the second wavelength band based on the raw water image data input to the image input unit 21, and identifies the identified pixel region as cyanobacteria. It is extracted as a kind of pixel region (step S102).

  The shape discrimination processing unit 23 discriminates the shape of the cyanobacteria pixel region extracted by the wavelength band processing unit 22, and extracts the rod-like cyanobacteria pixel region that is a cyanobacteria that generates musty odor (step S102). . The algae amount acquisition unit 24 counts the number of pixels in the pixel area of the cyanobacteria that generate musty odor extracted by the shape discrimination processing unit 23 and acquires the amount of cyanobacteria that generates the musty odor per unit amount of raw water ( Step S104).

  The musty odor estimation unit 27 refers to the musty odor estimation table 25 and estimates a rank indicating the degree of musty odor based on the amount of cyanobacteria that generate the musty odor acquired by the algae amount acquisition unit 24 (step S105). The display control unit 28 causes the display device 3 to display a rank indicating the degree of musty odor estimated by the musty odor estimating unit 27 (step S105).

  Since the musty odor estimation system 1 according to the first embodiment described above immediately displays a rank indicating the degree of musty odor of raw water on the display device 3 based on the raw water image data photographed by the imaging unit 15, compared with the conventional case. Thus, it is possible to shorten the time required to detect a bad odor such as musty odor in raw water. The musty odor estimation system 1 according to the first embodiment can detect the musty odor of raw water continuously at an arbitrary interval because the time required for detection of a strange odor can be several seconds to several tens of seconds. Since the musty odor estimation system 1 extracts algae based on the wavelength band, even when impurities other than algae such as gravel are mixed in the raw water, these impurities are not erroneously detected as algae. The reason why false detection can be prevented is that impurities such as gravel differ greatly in spectroscopic characteristics from algae. The musty odor estimation system 1 can display the amount of powdered activated carbon according to the degree of musty odor on the display device 3, so that a water purification plant administrator or an operator can input an appropriate amount of powdered activated carbon.

(Second Embodiment)
Next, a configuration example of the musty odor estimation apparatus 2A in the second embodiment will be described.
FIG. 6 is a diagram illustrating a configuration example of the musty odor estimation apparatus 2A according to the second embodiment. As shown in FIG. 6, the musty odor estimation device 2A differs from the musty odor estimation device 2 shown in FIG. 4 in that it includes an input processing unit 26A, a musty odor estimation unit 27A, and a musty odor information storage unit 29. Are the same. Therefore, the same components as those in FIG. The musty odor estimation apparatus 2A of the second embodiment accumulates information on the degree of musty odor estimated in the past with respect to the musty odor estimation apparatus 2 of the first embodiment, and stores the accumulated information on a new degree of musty odor. It is the structure which added the function used for estimation.

  The musty odor information storage unit 29 is associated with the date and time when the raw water image data was taken, information on the degree of musty odor estimated by the musty odor estimation unit 27A, and information on the raw water input from the input processing unit 26A (water temperature, PH value). And a musty odor information storage unit 29 for storing input information such as weather information. Moreover, the musty odor information storage unit 29 may further store information related to the amount of powdered activated carbon input according to the degree of musty odor. In the following description, information related to the past musty odor level stored in the musty odor information storage unit 29, information related to past raw water, and past weather information are collectively referred to as past information.

  Information to be stored in the musty odor estimation table 25 is input to the input processing unit 26A, as in the first embodiment. The input processing unit 26A also receives information used for the estimation process by the musty odor estimation unit 27A and information stored in the musty odor information storage unit 29. The information used for the estimation process in the musty odor estimation unit 27A and the information stored in the musty odor information storage unit 29 are, for example, information (water temperature, pH value) and weather information about raw water. Collectively referred to as input information.

  As in the first embodiment, the musty odor estimation unit 27A refers to the musty odor estimation table 25 and estimates the degree of musty odor based on the amount of cyanobacteria that generates the musty odor acquired by the algae amount acquisition unit 24. . Also, the musty odor estimation unit 27A adjusts the estimated degree of musty odor with reference to the input information from the input processing unit 26A and the past information stored in the musty odor information storage unit 29. For example, if it is determined that the degree of musty odor tends to change according to the water temperature based on past information, the musty odor estimation unit 27A may adjust the degree of musty odor estimated according to the water temperature. Also, for example, if it is determined that there is a difference in the activity of algae between the case where it is cloudy and the case where it is clear based on the past information and there is a tendency to affect the musty odor, the musty odor estimating unit 27A The degree of musty odor estimated according to the weather specified by the weather information may be adjusted.

Next, the operation of the musty odor estimation apparatus 2A in the second embodiment will be described.
FIG. 7 is a flowchart for explaining the operation of the musty odor estimating apparatus 2A of the second embodiment. The operation of the musty odor estimation apparatus 2A shown in FIG. 7 is the same as that of steps S101 to S104 as compared with the operation of the musty odor estimation apparatus 2 shown in FIG. Therefore, description of steps S101 to S104, which are the same processing, is omitted.

  When the algae amount acquisition unit 24 acquires the amount of cyanobacteria that generates musty odor in the process of step S104, the musty odor estimation unit 27A refers to the musty odor estimation table 25 and generates the musty odor acquired by the algae amount acquisition unit 24. Based on the amount of cyanobacteria, a rank indicating the degree of musty odor is estimated. The musty odor estimation unit 27A adjusts the estimated rank based on the past information referred from the musty odor information storage unit 29 and the input information from the input processing unit 26A, and shows the rank indicating the degree of the musty odor after adjustment. It outputs to the musty odor information storage unit 29 and the display control unit 28 (step S107).

  The musty odor information storage unit 29 stores information on the degree of the musty odor after adjustment received from the musty odor estimation unit 27A (step S108). The display control unit 28 causes the display device 3 to display a rank indicating the degree of musty odor estimated by the musty odor estimating unit 27 (step S105). The display control unit 28 may display related input information and past information on the display device 3 together with the rank indicating the degree of musty odor.

  As described above, the musty odor estimation apparatus 2A of the second embodiment adjusts the estimated degree of musty odor of raw water based on the input information and past information based on the raw water image data photographed by the imaging unit 15. can do. Thereby, the musty odor estimation apparatus 2A of the second embodiment can estimate the degree of musty odor of raw water with higher accuracy. In addition, when the past information includes information related to the past amount of powdered activated carbon, the musty odor estimation apparatus 2A obtains the amount of powdered activated carbon by using the past information and more accurately. The input amount of the powdered activated carbon can be estimated and displayed on the display device 3.

  The function of the musty odor estimation device 2 or the musty odor estimation device 2A may be integrated into the imaging unit 15 as a single chip. You may implement | achieve the function of the musty odor estimation apparatus 2 or the musty odor estimation apparatus 2A as an application which operate | moves on a personal computer or a portable information terminal. The musty odor estimation system 1 may be configured to transmit image data captured by the imaging unit 15 to a server via a network and realize the function of the musty odor estimation device 2 or the musty odor estimation device 2A on the server.

  The above-mentioned musty odor estimation system 1 is configured to detect an algae that generates a musty odor, but may be configured to detect algae that generate a fishy odor (such as urogrena and cryptomonas), that is, a musty odor estimation system. 1 should just be comprised so that the algae which generate | occur | produce a strange odor may be detected and the grade of a strange odor can be displayed. Moreover, since the structure of the raw | natural water imaging device 5 is the structure similar to the structure of the conventional turbidimeter, the turbidimeter which measures turbidity is provided in the periphery of the imaging part 15 in the raw | natural water imaging device 5, It is also possible to measure the turbidity of raw water. The musty odor estimation system 1 may use the measured turbidity as a parameter when estimating the degree of off-flavor.

(Modification of the first and second embodiments)
In addition, the imaging unit 15 may be configured with a hyperspectral camera that can acquire the signal intensity of each wavelength in increments of 5 to 10 nm at wavelengths of 400 nm to 900 nm, for example, with each pixel. When a hyperspectral camera is used as the imaging unit 15, most algae can be identified with high accuracy without performing processing relating to the shape of algae by the shape discrimination processing unit 23. Below, the musty odor estimation system 1 is demonstrated to an example about the method of processing the image data image | photographed with the imaging part 15 which is a hyperspectral camera, and specifying algae.

  FIG. 8 is a diagram illustrating an example of image processing for image data captured by the imaging unit 15 that is a hyperspectral camera. As a premise, the imaging unit 15 that is a hyperspectral camera photographs a plurality of algae such as “Anabaena”, “Oshiratoria”, “Formidium”, “Diatomae”, and “Microkistis”, respectively, as shown in FIG. Identify the spectroscopic spectrum of each algae. The imaging unit 15, which is a hyperspectral camera, obtains image data as shown in, for example, photographs 81 and 82 in FIG. 8 by photographing water including some of the plurality of algae described above. Next, in the photographs 81 and 82, the musty odor estimation system 1 performs a discriminant analysis for discriminating pixels having similar spectral spectra from pixels indicating the same kind of algae. When the musty odor estimation system 1 outputs pixels indicating the same type of algae with the same color, an analysis image 83 based on the photograph 81 is output, and an analysis image 84 based on the photograph 82 is output.

  As shown in the analysis images 83 and 84, the same type of algae are colored with the same color, and it can be seen that the type of algae can be discriminated. In particular, “anabena”, “oshiratoria”, and “formidium” that generate musty odor can be distinguished.

  Depending on the type of algae, the amount contained in 1 ml (milliliter) of water that people begin to feel musty odor may vary. For example, formidium often feels musty at 1000 pieces / ml or more, and anabena often feels musty at 100 pieces / ml or more. Therefore, by specifying the type of algae as described above, if the amount of algae can be acquired for each type of algae, the degree of musty odor can be estimated with higher accuracy.

  In each of the above embodiments, each functional unit in the musty odor estimation apparatus 2 or the musty odor estimation apparatus 2A is a software function unit, but may be a hardware function unit such as an LSI.

  According to at least one embodiment described above, the degree of musty odor contained in the raw water can be presented based on the amount of algae that cause the off-flavor contained in the raw water. Moreover, you may display the quantity of the powdered activated carbon according to the degree of musty odor. This makes it possible to optimally control the amount of powdered activated carbon according to the degree of musty odor, compared to the conventional method in which the amount of powdered activated carbon is determined empirically. The musty odor estimation system 1 can be configured to be portable enough to be carried, and can be introduced into an existing water purification plant without a large-scale construction. In addition, by controlling the amount of powdered activated carbon input optimally, wasteful powdered activated carbon can be prevented from being charged and the cost can be reduced.

  In addition, when the functions in the musty odor estimation apparatus 2 or the musty odor estimation apparatus 2A described above are realized by software, a program for realizing these functions is recorded on a computer-readable recording medium, and the program is stored. The program may be loaded into a computer system and executed. Here, the “computer system” includes hardware such as an OS (Operating System) and peripheral devices. The “computer-readable recording medium” refers to a portable medium such as a flexible disk, a magneto-optical disk, a ROM, a CD (Compact Disk) -ROM, or a storage device such as a hard disk built in the computer system. . Furthermore, “computer-readable recording medium” means a volatile memory (RAM) inside a computer system that becomes a server or client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line. In addition, those holding programs for a certain period of time are also included.

In addition, the above program may be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the “transmission medium” for transmitting the program refers to a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line.
Further, the above program may be for realizing a part of the functions described above. Further, the program may be a so-called difference file (difference program) that can realize the above-described functions in combination with a program already recorded in the computer system.

  Although several embodiments of the present invention have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and the equivalents thereof.

DESCRIPTION OF SYMBOLS 1 ... Mold odor estimation system 2, 2A ... Mold odor estimation apparatus, 3 ... Display apparatus, 4 ... Input device, 5 ... Raw water imaging device, 6 ... Cable, 13 ... Tray for imaging | photography, 15 ... Imaging part, 16 ... Optical system, DESCRIPTION OF SYMBOLS 17 ... Light source, 21 ... Image input part, 22 ... Wavelength band process part, 23 ... Shape discrimination | determination process part, 24 ... Algae quantity acquisition part, 25 ... Moldy smell estimation table, 26, 26A ... Input processing part, 27, 27A ... Moldy smell Estimating unit, 28 ... display control unit, 29 ... musty odor information storage unit

Claims (9)

An image input unit that receives image data of raw water as the subject from an imaging unit that photoelectrically converts light from the subject for each of a plurality of wavelength bands;
Based on the signal intensity of the plurality of wavelength bands in the image data input to the image input unit, a pixel region of the first algae contained in the raw water is extracted, and the extracted pixel region of the first algae Algae amount acquisition unit for acquiring the amount of the first algae per unit amount of the raw water,
An off-flavor estimation unit that estimates the level of off-flavor of the raw water based on the amount of the first algae acquired by the algae amount acquisition unit;
An off-flavor estimation device comprising: The algae amount acquisition unit
A wavelength band processing unit for extracting a pixel region of the second algae based on the signal intensity of the plurality of wavelength bands;
A shape discriminating unit that discriminates the shape of the pixel region of the second algae extracted by the wavelength band processing unit and extracts the pixel region of the first algae;
The odor estimation apparatus according to claim 1, comprising: The imaging unit photoelectrically converts light in a first wavelength band and a second wavelength band corresponding to a wavelength band of light absorbed by the first algae,
The said wavelength band process part extracts the pixel area | region of the said 2nd algae contained in the said raw water based on the signal intensity of the said 1st wavelength band, and the signal intensity of the said 2nd wavelength band. The odor estimation apparatus as described. The first wavelength band is a wavelength band of 650 nm to 700 nm,
The off-flavor estimation apparatus according to claim 3, wherein the second wavelength band is a wavelength band of 600 nm to 650 nm. The wavelength band processing unit has arbitrary coefficients m and n (−1 ≦ 1), where A is the signal intensity of the first wavelength band output from the imaging unit, and B is the signal intensity of the second wavelength band. The following formula for obtaining an algal index indicating the first algae using m ≦ 1, −1 ≦ n ≦ 1): Algae index = (mA−nB) / (mA + nB)
The off-flavor estimation apparatus according to claim 3, wherein the alga index is calculated to obtain a pixel region of the second algae based on the acquired algae index. The second algae is cyanobacteria,
The off-odor estimation apparatus according to any one of claims 2 to 5, wherein the first algae is a cyanobacteria causing the off-flavor. An imaging unit that photoelectrically converts light from raw water as a subject for each of a plurality of wavelength bands;
The off-flavor estimation device according to any one of claims 1 to 6,
A nasty smell estimation system. An image input step in which image data having raw water as the subject is input from an imaging unit that photoelectrically converts light from the subject for each of a plurality of wavelength bands;
Based on the signal intensity of the plurality of wavelength bands in the image data input in the image input step, a pixel region of the first algae contained in the raw water is extracted, and the extracted pixel region of the first algae Algae amount obtaining step for obtaining the amount of the first algae per unit amount of the raw water based on
An off-flavor estimation step for estimating the off-flavor level of the raw water based on the amount of the first algae acquired in the algae amount acquisition step;
A method for estimating off-flavors. An image input step in which image data having raw water as the subject is input from an imaging unit that photoelectrically converts light from the subject for each of a plurality of wavelength bands;
Based on the signal intensity of the plurality of wavelength bands in the image data input in the image input step, a pixel region of the first algae contained in the raw water is extracted, and the extracted pixel region of the first algae Algae amount obtaining step for obtaining the amount of the first algae per unit amount of the raw water based on
An off-flavor estimation step for estimating the off-flavor level of the raw water based on the amount of the first algae acquired in the algae amount acquisition step;
A strange odor estimation program to make a computer execute.