JP6710591B2 - Off-odor estimating device, off-odor estimating system, off-odor estimating method and off-odor estimating program - Google Patents

Description

Embodiments of the present invention relate to an offensive odor estimation device, an offensive odor estimation system, an offensive odor estimation method, and an offensive odor estimation program.

In recent years, with the progress of water purification technology, the offensive odor damage such as musty odor of tap water is decreasing. However, although it is a small number, the offensive odor damage still occurs. As a measure for musty odor, waterworks companies are introducing advanced water purification treatment and introducing powdered activated carbon. The powdered activated carbon is empirically predicted to generate a musty odor according to the season and climate. In this way, based on empirical prediction, when powdered activated carbon is added, it is necessary to add a large amount of powdered activated carbon in consideration of the low accuracy of prediction. That is, there is a problem that it is necessary to add powdered activated carbon more than necessary, and the powdered activated carbon cannot be used efficiently.

In order to use the powdered activated carbon efficiently, it is possible to detect the generation of musty odor and control the addition of the powdered activated carbon according to the generation of musty odor. As a method for detecting the generation of musty odor, there is a musty odor detection device that uses gas chromatography mass spectrometry (GCMS method) for directly measuring the musty odor substances geosmin and 2-methylisoborneol (2-MIB) from raw water. .. However, the above musty odor detecting device is very expensive, and thus has not been introduced into water purification plants. Further, the above musty odor detection device requires a lot of time for one measurement. If it takes a long time to perform one measurement, various problems may occur, such as being unable to cope with a sudden increase in musty odor.

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

Japanese Patent Laid-Open No. 09-75918

An object of the present invention is to provide an offensive odor estimation device, an offensive odor estimation system, an offensive odor estimation method, and an offensive odor estimation program that can reduce the time required to detect an offensive odor in raw water.

The offensive odor estimation device of the embodiment includes an image input unit, an algae amount acquisition unit, a storage unit, and an offensive odor estimation unit. The image input unit receives image data of raw water as an object from an imaging unit that photoelectrically converts light from the object 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 for each type of the first algae based on the signal intensities of the plurality of wavelength bands in the image data input to the image input unit. Then, the amount of each type of the first algae per unit amount of raw water is acquired based on the extracted pixel region of each type of the first algae. The storage unit stores the evaluation value of the generated odor for each type of the first alga. The offensive odor estimating unit estimates the degree of offensive odor of the raw water based on the amount of the first algae obtained by the algae amount obtaining unit for each type and the evaluation value stored in the storage unit.

The figure which shows the structural example of the musty odor estimation system 1 in this embodiment. The figure which shows the photograph example of the algae which generate|occur|produces in raw water. The figure which shows the spectroscopy spectrum of "diatom", "microcystis", "anabena", "oscilatoria", and "formidium". The figure which shows the structural example of the musty odor estimation apparatus 2 of 1st Embodiment. The flow figure explaining operation|movement of the musty odor estimation apparatus 2 of 1st Embodiment. The figure which shows the spectrum of "contaminants". The figure which shows the structural example of the musty odor estimation apparatus 2 provided with the foreign matter detection part 31. The figure which shows the structural example of the musty odor 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 imaged with the imaging part 15 which is a hyper spectrum camera. The figure which shows the structural example of the musty odor estimation system 1 in the modification of this embodiment.

Hereinafter, an offensive odor estimating device, an offensive odor estimating system, an offensive odor estimating method, and an offensive odor estimating program according to embodiments will be described with reference to the drawings.

(Outline)
First, the outline of the offensive odor estimation system according to the present embodiment will be described. In the following description, a musty odor estimation system that estimates the degree of musty odor will be described as a specific example of the offensive odor estimation system.
FIG. 1 is a diagram showing a configuration example of a musty odor estimating system 1 in the present embodiment. As shown in FIG. 1, the musty odor estimating system 1 includes a musty odor estimating device 2, a display device 3, an input device 4, a raw water photographing device 5, and a cable 6.

The raw water photographing device 5 includes a water collecting port 11, a water collecting pipe 12, a photographing 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. Equipped with. The water sampling port 11 is an inflow port for sampling a part of the raw water taken from the water source. The raw water flowing in from the water intake 11 is subjected to water pressure due to a difference in height, a pump, etc., and flows in the direction of the arrow. The water sampling pipe 12 is a pipe through which the raw water flowing from the water sampling port 11 flows.

The imaging tray 13 is a tray in which the raw water that has passed through the water sampling pipe 12 is collected. The imaging tray 13 is also a tray for storing raw water to be imaged by the imaging unit 15. In the imaging tray 13, at least in an area to be imaged by the imaging unit 15, if the raw water is transparent, for example, a white member is arranged so as to uniformly reflect the light from the light source 17 regardless of the wavelength. There is.

The light shielding wall 14 is, for example, a box-shaped one having an upper surface on which the imaging unit 15 and the light source 17 are installed and a side surface supporting the upper surface. The light blocking wall 14 has a function of blocking the raw water on the imaging tray 13 captured by the imaging unit 15 from being exposed to light (external light or the like) other than the light from the light source 17. The light shielding wall 14 is coated with an antireflection film on the inner wall surface thereof, for example, in order to suppress the influence of flare, stray light, or the like during imaging.

The image pickup unit 15 includes an image pickup element in which pixels having a sensitivity capable of discriminating a peak of signal intensity in a plurality of specific wavelength bands are arranged. The image pickup device included in the image pickup unit 15 has a configuration in which pixels for photoelectrically converting light from a subject are arranged, and may be a single plate or multiple plates. The specific wavelength is a plurality of wavelength bands effective for specifying cyanobacteria that generate musty odor, for example, 650 nm to 700 nm (first wavelength band) and 600 nm to 650 nm (second wavelength band). Is. The plurality of wavelength bands may be 660 nm to 690 nm (first wavelength band) and 610 nm to 640 nm (second wavelength band). The plurality of wavelength bands may be 670 nm to 680 nm (first wavelength band) and 620 nm to 630 nm (second wavelength band). Further, a pixel having sensitivity capable of discriminating peaks of signal intensity in a plurality of specific wavelength bands means a plurality of wavelengths capable of discriminating that there are peaks of signal intensity in the first wavelength band and the second wavelength band. It is a pixel having a band sensitivity. The plurality of wavelength bands capable of discriminating that the first wavelength band and the second wavelength band have a spectrum peak 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 sensitivities in these plural wavelength bands and outputs the processed signals as raw water image data. The raw water image data may be, for example, data having values of received light intensity in a plurality of wavelength bands in each pixel. For example, for each pixel, the value of the received light intensity of the light in the first wavelength band, the value of the received light intensity of the light in the second wavelength band, and the value of the received light intensity of the light in the wavelength band close to these wavelength bands , May be given. Thereby, the musty odor estimation device 2 has a spectrum peak 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 imaging tray 13. The optical system 16 may be composed of a microscope when the object to be photographed is photographed at a high magnification. The light source 17 is, for example, an illumination device that irradiates a subject with white light. The imaging unit 15 outputs to the musty odor estimation device 2 raw water image data, which is image data including spectrum data of a specific wavelength in each pixel obtained by photographing the raw water on the imaging tray 13 via the cable 6. To do.

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

The musty odor estimation device 2 is a computer that is connected to the image pickup unit 15 of the raw water image pickup device 5 via a cable 6 and has a function of acquiring raw water image data from the image pickup unit 15. Further, the musty odor estimating device 2 has a function of outputting musty odor information, which is information that estimates the degree of musty odor of raw water based on the acquired raw water image data. Further, the musty odor estimating device 2 has a function of classifying the raw water image data for each pixel having similar spectrum data. The musty odor estimating device 2 may have a function of performing color-coding processing on the classified pixels.

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 needed. The input device 4 is an input device such as a keyboard and a mouse connected to the musty odor estimating device 2 which is a computer.

The input device 4 may be a device to/from which a recording medium connected to the musty odor estimating device 2 which is a computer can be attached/detached and which can read data from the attached recording medium. Thereby, the input device 4 can input the data recorded on the recording medium. The touch panel terminal may be used to integrate the musty odor estimating device 2, the display device 3, and the input device 4.

Here, a method of selecting a specific wavelength band will be described by showing a specific example. Various algae that produce musty odor are captured in a specific wavelength band with a hyperspectral camera that can acquire the signal intensity of each wavelength of 5 nm to 10 nm in the wavelength range of 400 nm to 1000 nm for each alga, and must have a musty odor. Identify the spectroscopic spectrum of algae. Then, if there is a wavelength band in which the spectral spectrum values significantly differ between cyanobacteria that generate musty odor and other algae, that wavelength band may be set as the specific wavelength band. Further, if there are a plurality of peak wavelengths peculiar to cyanobacteria that produce musty odors, the pixel area of the cyanobacteria that produces musty odors may be specified by using the values of a plurality of wavelength bands that respectively include the plurality of peak wavelengths. ..

Blue-green algae that cause musty odor include "Anabaena", "Osilatoria" and "Formidium", and blue-green algae that do not cause musty odor include "Microcystis". FIG. 2 is a diagram showing a photograph example of algae generated in raw water. In FIG. 2, a photograph 41 is “Ocylatria”, which is a cyanobacteria that produces a musty odor. Photo 42 shows "Anabaena", a cyanobacteria that produces musty odor. Photo 43 shows "Formidium", a cyanobacteria that produces musty odor. Photo 44 shows "diatom", which is one of the algae other than cyanobacteria that does not produce musty odor. Photo 45 shows "Microcystis", a blue-green alga that does not produce musty odor.

As shown in FIG. 2, "anabena", "oscilatoria", and "formidium", which are musty odor-producing cyanobacteria, have a rod-like shape. "Microcystis," which is a cyanobacteria that does not generate musty odor, and "diatom," which is one of the algae that does not generate musty odor, has a spherical shape.

FIG. 3 is a diagram showing the spectrum of "diatom", "microcystis", "anabena", "oscilatoria", and "formidium". As shown in FIG. 3, the blue-green algae “Microcystis”, “Anabaena”, “Osylatoria”, and “Formidinium” are around 620 nm (frame 51 in FIG. 3) and 680 nm (frame 52 in FIG. 3). There is a peak of the spectrum (downward peak in FIG. 3). The “diatom” has spectral peaks (downward peaks in FIG. 3) near 500 nm and around 680 nm. In FIG. 3, a frame 51 indicating a wavelength band near 620 nm and a frame 52 indicating a wavelength band near 680 nm are shown. 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 around 620 nm appears mainly due to the absorption of light by a dye called phycocyanin. It is considered that the peak around 680 nm appears mainly due to the absorption of light by the pigment called chlorophyll a. Thus, the wavelength band corresponding to the pigment contained in algae becomes the peak of the spectrum. Therefore, if there is a pigment unique to algae that produces a musty odor or the like, the wavelength band of light absorbed by the pigment becomes the wavelength band unique to the algae. There are few algae other than cyanobacteria that have phycocyanin, and phycocyanin can be said to be a pigment unique to cyanobacteria.

Considering the characteristics of the algae described above, first, in the second wavelength band including 620 nm and the first wavelength band including 680 nm, which is the peak wavelength band specific to cyanobacteria output from the imaging unit 15, The signal strength is used to identify whether the pixel region is a cyanobacteria. Next, it is considered that the raw water image data output from the imaging unit 15 can be subjected to image processing to specify a pixel region of cyanobacteria that produces a musty odor according to the shape of the algae identified as the pixel region of cyanobacteria. The musty odor estimating apparatus 2 of the present embodiment specifies a pixel region of cyanobacteria by the wavelength band based on the raw water image data output from the imaging unit 15, and then determines a rod shape based on the shape of the pixel region of cyanobacteria. Processing is performed to determine the pixel area of cyanobacteria as a pixel area of cyanobacteria that produces musty odor.

Next, the flow of the musty odor estimation process in the musty odor estimation system 1 will be described.
For example, when raw water is caused to flow into the pipe connected to the water intake port 11 at a predetermined water pressure, the raw water is collected in the imaging tray 13 via the water intake port 11 and the water intake 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 estimating 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 estimating apparatus 2 according to the first embodiment will be described.
FIG. 4 is a diagram showing a configuration example of the musty odor estimating apparatus 2 of the first embodiment. As shown in FIG. 4, the musty odor estimation device 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 estimating unit 27 and a display control unit 28 are provided.

The image input unit 21 is connected to the cable 6 and receives the raw water image data captured by the image capturing unit 15 in the raw water capturing device 5. The wavelength band processing unit 22 extracts a cyanobacterial pixel region from the raw water image data based on the signal intensities 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. The wavelength band processing unit 22 specifies, for example, a pixel having peaks in the first wavelength band and the second wavelength band, and extracts the specified pixel region as a cyanobacterial pixel region.

The wavelength band processing unit 22, for example, when the signal strength of the first and second wavelength bands is lower than the signal strength of the wavelength bands before and after the first and second wavelength bands, It is determined to have a peak in the wavelength band. Here, the front and rear wavelength bands are a wavelength band having a wavelength shorter than the first and second wavelength bands and a wavelength band having a wavelength longer than the first and second wavelength bands. In addition, in some cases, the wavelength band processing unit 22 determines, for example, 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-described peak determination method is an example, and the wavelength band processing unit 22 determines the peaks in the first wavelength band and the second wavelength band for each pixel based on the change in the signal intensity for each wavelength band. Determine the presence or absence.

The extraction process of the cyanobacterial pixel region in the wavelength band processing unit 22 is not limited to the process of extracting based on the presence/absence of peaks in the first wavelength band and the second wavelength band. You may use the extraction process shown in. The wavelength band processing unit 22 calculates the cyanobacterial index using the following (Equation 1) based on the pixel data A of the first wavelength band and the pixel data B of the second wavelength band, for example.
Cyanobacterial index=(mA−nB)/(mA+nB) (Equation 1)

However, m and n are arbitrary coefficients, and for the purpose of improving the accuracy of the cyanobacterial index, for example, are coefficients from -1 to 1 (-1 ≤ m ≤ 1, -1 ≤ n ≤ 1). ). For the coefficients m and n, the values of A and B actually measured for a plurality of types of algae are input, and based on the results of changing the values of the coefficients m and n, cyanobacteria and other algae are most determined. Find a value that can be accurately determined. In this case, the wavelength band processing unit 22 determines whether or not the pixel area of cyanobacteria is based on the calculated cyanobacterial index, and extracts the pixel area of cyanobacteria.

The shape determination processing unit 23 determines the shape of the pixel region of cyanobacteria extracted by the wavelength band processing unit 22 and produces rod-shaped cyanobacteria that are musty-smelling cyanobacteria (Ocillatoria, Anabaena, Formidium). The pixel area of is extracted. Specifically, it is determined whether the rod-shaped cyanobacteria shown in photographs 41 to 43 of FIG. 2 or the spherical cyanobacteria shown in photograph 45. The determination method in the shape determination 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 region of the rod-shaped cyanobacteria (=cyanobacteria that produces musty odor) extracted by the shape determination processing unit 23, and generates the musty odor per unit amount of raw water. Get information on the amount of cyanobacteria. The algae amount acquisition unit 24 is, for example, a shooting target of the raw water image data in consideration of the depth of the shooting tray 13 and the shooting range of the imaging unit 15 (or the area serving as the shooting range of the shooting 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 stored in advance in a storage device such as a magnetic hard disk device or a semiconductor storage device. Such a storage device is provided by the musty odor estimating device 2. 5 is a table in which the amount of cyanobacteria that produces a 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, a person judging the degree of musty odor of each, and classifying into multiple ranks to determine the amount of cyanobacteria. A table corresponding to the rank indicating the degree of musty odor is created. Here, the rank indicating the degree of musty odor corresponds to, for example, the unit of the amount of powdered activated carbon added to the raw water. With this, the musty odor estimation device 2 notifies the rank of the musty odor to the manager of the water purification plant, so that the manager of the water purification plant can easily grasp the optimum amount of activated carbon powder to be input.

The method of creating the table is not limited to this, and a person may use gas chromatography mass spectrometry (GCMS method) to measure the degree of musty odor and classify into a plurality of ranks. Further, in addition to expressing the degree of musty odor by rank, if there is a measured value measured by gas chromatography mass spectrometry, the measured value may be a table in which the amount of cyanobacteria is associated. ..

In addition to the amount of cyanobacteria that generate musty odor per unit amount of raw water, the musty odor estimation table 25 indicates the degree of musty odor in the information such as the water temperature of the raw water, the PH value, and the climate when the raw water is sampled. Information determined to be affected may be stored. When it is determined that the water temperature influences the degree of musty odor, the method of creating the table is, for example, changing the amount of cyanobacteria per unit amount of water, and changing the water temperature in each amount of cyanobacteria. In this case, a person judges the degree of musty odor 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 with each other.

The input processing unit 26 receives an input of data or the like from the input device 4. The input processing unit 26 causes the musty odor estimation table 25 to record the table itself input from the input device 4 and stored in the musty odor estimation table 25, data in the table, and the like.

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 produces musty odor acquired by the algae amount acquisition unit 24. The musty odor estimating unit 27 specifies the degree of musty odor corresponding to the amount of musty odor-producing cyanobacteria from the musty odor estimation table 25, thereby estimating the degree of musty odor according to the amount of musty odor producing cyanobacteria. 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 the 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 the input amount of powdered activated carbon corresponding to the rank indicating the degree of musty odor. Thereby, the display control unit 28 can display the amount of powdered activated carbon according to the rank on the display device 3 together with the rank indicating the degree of musty odor.

Next, the operation of the musty odor estimating apparatus 2 according to the first embodiment will be described.
FIG. 5: is a flowchart explaining operation|movement of the musty odor estimation apparatus 2 of 1st Embodiment. The raw water image data captured by the image capturing unit 15 is input to the image input unit 21 (step S101). The wavelength band processing unit 22 specifies 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 sets the region of the specified pixel in cyanobacteria. It is extracted as a pixel region of a class (step S102).

The shape determination processing unit 23 determines the shape of the cyanobacterial pixel region extracted by the wavelength band processing unit 22 and extracts a rod-shaped cyanobacterial pixel region that is a cyanobacteria that produces musty odor (step S102). .. The algae amount acquisition unit 24 counts the number of pixels in the pixel region of the cyanobacteria that produces a musty odor extracted by the shape determination processing unit 23, and acquires the amount of cyanobacteria that produces a 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 the rank indicating the degree of musty odor based on the amount of cyanobacteria that produces 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 the rank indicating the degree of musty odor estimated by the musty odor estimating unit 27 (step S105).

Since the musty odor estimation system 1 of the first embodiment described above immediately displays the rank indicating the musty odor degree of raw water on the display device 3 based on the raw water image data taken by the imaging unit 15, it is different from the conventional one. As a result, the time required to detect an offensive odor such as musty odor in raw water can be shortened. Since the musty odor estimation system 1 of the first embodiment can set the time required for detecting an offensive odor to several seconds to several tens of seconds, the musty odor of raw water can be continuously detected at arbitrary intervals. 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, the impurities are rarely erroneously detected as algae. The reason why erroneous detection can be prevented is that impurities such as gravel have spectral characteristics greatly different from those of algae. Since the musty odor estimation system 1 can display the input amount of powdered activated carbon according to the degree of musty odor on the display device 3, the administrator or worker of the water purification plant can input an appropriate amount of powdered activated carbon.

FIG. 6 is a diagram showing spectroscopic spectra of algae (diatoms, microcystis, anabaena) and “contaminants (gravel)”. The contaminants are substances (noise substances) different from algae, such as gravel, tree branches, and artificial substances. The vertical axis represents the transmittance and the horizontal axis represents the wavelength of light. As is clear from FIG. 6, the characteristics of the spectroscopic spectrum of contaminants (gravel) are significantly different from the characteristics of the spectroscopic spectrum of algae. Further, the characteristic of the spectral spectrum of the contaminant (gravel) is significantly different from the characteristic of the spectral spectrum of the pixel in which only water is visually visible. Therefore, it is possible to detect the pixel in which the contaminant is imaged by using the value of the received light intensity in the specific wavelength band. Based on such characteristics, the musty odor estimating apparatus 2 may further include a contaminant detecting unit that detects contaminants contained in the raw water. FIG. 7: is a figure which shows the structural example of the musty odor estimation apparatus 2 provided with such a foreign matter detection part 31. The contaminant detection unit 31 may detect the amount and ratio of contaminants per unit amount of raw water. With such a configuration, the musty odor estimating device 2 can estimate the degree of odor of raw water and detect impurities contained in the raw water.

(Second embodiment)
Next, a configuration example of the musty odor estimating apparatus 2A according to the second embodiment will be described.
FIG. 8: is a figure which shows the structural example of the musty odor estimation apparatus 2A of 2nd Embodiment. As shown in FIG. 8, the musty odor estimating apparatus 2A differs from the musty odor estimating apparatus 2 shown in FIG. 4 in that it includes an input processing unit 26A, a musty odor estimating unit 27A, and a musty odor information storage unit 29, and other configurations. Are the same. Therefore, the same reference numerals as those in FIG. 4 are given to those having the same configuration, and the description thereof will be omitted. The musty odor estimating apparatus 2A of the second embodiment accumulates information on the degree of musty odor estimated in the past with respect to the musty odor estimating apparatus 2 of the first embodiment, and uses the accumulated information as a new degree of musty odor. This is a configuration in which a function used for estimation is added.

The musty odor information storage unit 29 associates the date and time when the raw water image data was captured with the information about the degree of musty odor estimated by the musty odor estimating unit 27A and the information about 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. In addition, the musty odor information storage unit 29 may further store information about the amount of the powdered activated carbon that has been added according to the degree of musty odor. In the following description, the information about the past degree of musty odor, the information about past raw water, and the past weather information stored in the musty odor information storage unit 29 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. Further, the input processing unit 26A also receives the information used in the estimation process by the musty odor estimating unit 27A and the information stored in the musty odor information storage unit 29. Note that the information used in the estimation process by the musty odor estimating unit 27A and the information stored in the musty odor information storage unit 29 are, for example, information about raw water (water temperature, PH value) and weather information. Collectively called input information.

As in the first embodiment, the musty odor estimating unit 27A refers to the musty odor estimating table 25 and estimates the degree of musty odor based on the amount of cyanobacteria that produces musty odor acquired by the algae amount acquiring unit 24. .. Further, the musty odor estimating unit 27A adjusts the estimated degree of musty odor by referring 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 based on the past information that the degree of musty odor tends to change according to the water temperature, the musty odor estimation unit 27A may adjust the degree of musty odor estimated according to the water temperature. Further, for example, if it is determined based on the past information that there is a difference in the activity of the algae depending on whether it is cloudy or sunny, and the tendency to affect the musty odor, the musty odor estimating unit 27A determines The degree of musty odor estimated based on the weather specified by the weather information may be adjusted.

Next, the operation of the musty odor estimating apparatus 2A according to the second embodiment will be described.
FIG. 9 is a flowchart illustrating the operation of the musty odor estimating apparatus 2A according to the second embodiment. The operation of the musty odor estimating apparatus 2A illustrated in FIG. 9 is the same as the operation of the musty odor estimating apparatus 2 illustrated in FIG. 5 in the processes of steps S101 to S104. Therefore, the description of steps S101 to S104, which are the same processes, is omitted.

When the algae amount acquisition unit 24 acquires the amount of cyanobacteria that produces a 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 obtained by the algae amount acquisition unit 24. Estimate the rank indicating the degree of musty odor based on the amount of cyanobacteria. The musty odor estimating unit 27A performs adjustment on the estimated rank based on the past information referred to from the musty odor information storage unit 29 and the input information from the input processing unit 26A, and indicates the rank indicating the degree of the musty odor after adjustment, The data is output to the musty odor information storage unit 29 and the display control unit 28 (step S107).

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

As described above, the musty odor estimating apparatus 2A according to the second embodiment adjusts the degree of the musty odor of the estimated raw water based on the raw water image data captured by the imaging unit 15 based on the input information and the past information. can do. As a result, the musty odor estimating apparatus 2A of the second embodiment can more accurately estimate the degree of musty odor of raw water. Further, when the past information includes information on the past input amount of the powdered activated carbon, the musty odor estimating device 2A obtains the powdered activated carbon input amount also by using the past information, and is more accurate. The input amount of the powdered activated carbon can be estimated and displayed on the display device 3.

The function of the musty odor estimating device 2 or the musty odor estimating device 2A may be integrated into one chip and incorporated in the imaging unit 15. The function of the musty odor estimating device 2 or the musty odor estimating device 2A may be realized as an application running on a personal computer or a mobile information terminal. The musty odor estimation system 1 may be configured to transmit the image data captured by the imaging unit 15 to a server via a network and realize the functions of the musty odor estimation device 2 or the musty odor estimation device 2A on the server.

The musty odor estimation system 1 described above has a configuration that detects algae that produce a musty odor, but it may have a configuration that detects algae that produce a fishy odor (such as uroglena and cryptomonas), that is, a musty odor estimation system. 1 may be configured to detect algae that generate an offensive odor and display the degree of the offensive odor. Further, since the structure of the raw water photographing device 5 is the same as the structure of the conventional turbidity meter, a turbidity meter for measuring the turbidity is provided in the raw water photographing device 5 in the vicinity of the image pickup unit 15. It is also possible to measure the turbidity of raw water. Then, the musty odor estimation system 1 may use the measured turbidity as a parameter when estimating the degree of the offensive odor.

(Modifications of the first and second embodiments)
Further, the image capturing unit 15 may be configured by a hyper-spectral camera capable of acquiring the signal intensity of each wavelength of 5 nm to 10 nm in the wavelength of 400 nm to 900 nm at each pixel. If a hyperspectral camera is used as the imaging unit 15, most algae can be accurately specified without performing the processing on the shape of algae by the shape determination processing unit 23. Hereinafter, a method of processing image data captured by the imaging unit 15 which is a hyperspectral camera to identify algae will be described with the musty odor estimation system 1 as an example.

FIG. 10 is a diagram showing an example of image processing performed on image data captured by the image capturing unit 15, which is a hyperspectral camera. As a premise, the imaging unit 15, which is a hyperspectral camera, photographs a plurality of algae such as "anavena", "oscilatoria", "formidium", "diatom", and "microcystis", respectively, as shown in FIG. The spectroscopic spectrum of each algae. The imaging unit 15, which is a hyperspectral camera, obtains image data as shown in photographs 81 and 82 of FIG. 10, for example, by photographing water containing some of the plurality of algae described above. Next, in Photos 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 type of algae. The musty odor estimation system 1 outputs the analysis image 83 based on the photograph 81 and the analysis image 84 based on the photograph 82 when the pixels indicating the same type of algae are colored and output with the same color.

As shown in the analysis images 83 and 84, it is understood that the same type of algae is colored with the same color, and the type of algae can be discriminated. In particular, "anabena", "oscilatoria", and "formidium" that produce musty odor can be identified.

Depending on the type of algae, the amount contained in 1 ml (ml) of water at which a person begins to feel a musty odor may vary. For example, formidium often feels a musty odor at 1000 pieces/ml or more, and Anavena often feels a musty smell at 100 pieces/ml or more. Such a difference occurs because the amount of odorous substances generated varies depending on the type of algae. For example, there is an experimental result that the generation amount of the odorous substance of Oshiratoria is 37 pg/500 μm, the generation amount of the odorous substance of Anabaena is 28 pg/500 μm, and the generation amount of the odorous substance of Formidium is 0.32 pg/500 μm. In this way, the amount of odorous substances generated varies greatly depending on the type of algae. Based on such a fact, a table in which the types of algae are associated with the odor evaluation value for each predetermined amount may be generated in advance. The odor evaluation value for each predetermined amount may be, for example, a value indicating the intensity of the odor generated by a predetermined amount of algae. By storing such a table as the musty odor estimation table 25 in advance, the musty odor estimation device 2 identifies the type of algae as described above, and determines the type of algae for each type of algae per unit amount of raw water. It becomes possible to estimate the degree of musty odor with higher accuracy by acquiring the amount of. It should be noted that it is not always necessary to use a hyperspectral camera to specify the type of algae. For example, by using the peak intensity of the received light intensity in a plurality of wavelength bands of 3 or more as the feature amount, it is possible to distinguish the types of algae (for example, Osylatoria, Anabaena, and Formidium).

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

FIG. 11: is a figure which shows the structural example of the musty odor estimation system 1 in the modification of this embodiment. As shown in FIG. 11, the musty odor estimating system 1 in the modified example includes a musty odor estimating device 2, a display device 3, an input device 4, a raw water photographing device 5 a, and a cable 6. The raw water photographing device 5a includes a water sampling port 11, a water sampling pipe 12, a light shielding wall 14a, an imaging unit 15a, an optical system 16, a light source 17a, a drain pipe 18, a drain port 19, and a transmission wall 20. Prepare The water sampling port 11, the water sampling pipe 12, the optical system 16, the drainage pipe 18, and the drainage port 19 have the same configuration as the example shown in FIG.

The light shielding wall 14a is configured such that the light reaching the image pickup unit 15a is only the light emitted from the light source 17a. The light shielding wall 14a is configured as a wall that covers the surroundings so as to include the light source 17a, the imaging unit 15a, the optical system 16, and the transmission wall 20, for example.

The image capturing unit 15a and the light source 17a are different from the image capturing unit 15 and the light source 17 in FIG. 1 in that they are arranged so as to face each other via the transmission wall 20. The imaging unit 15a is installed on the opposite side of the light source 17a via the transmission wall 20. The imaging unit 15a photographs the light emitted from the light source 17a and transmitted through the transmission wall 20 and condensed by the optical system 16.

The permeable wall 20 is configured to form a space in which a predetermined amount of raw water is retained. For example, two transmission walls 20 may be provided between the light source 17a and the imaging unit 15a with a predetermined gap. In this case, a space surrounded by the transmission wall 20 and the light shielding wall 14a may be formed by contacting the two transmission walls 20 with the light shielding wall 14a. Raw water that has flowed in through the water sampling pipe 12 is retained in the space. The raw water that has stopped in the space is then drained from the drain pipe 18 and the drain port 19.

In the raw water photographing device 5a configured as described above, the light of the light source 17a passes through the transmission wall 20 and the raw water existing in the space, and reaches the imaging unit 15a via the optical system 16. In this way, raw water image data is generated based on the intensity of the light that has reached the imaging unit 15a.

According to at least one embodiment described above, it is possible to present how much musty odor is contained in raw water, based on the amount of algae causing the offensive odor contained in raw water. In addition, the amount of powdered activated carbon according to the degree of musty odor may be displayed. As a result, it is possible to optimally control the input amount of the powdered activated carbon in accordance with the degree of musty odor, as compared with the conventional method in which the input amount of the powdered activated carbon is empirically predicted. The musty odor estimation system 1 can be configured compact enough to be carried, and can be installed in an existing water purification plant without large-scale construction. Further, by controlling the amount of the powdered activated carbon to be optimally controlled, it is possible to prevent the wasteful activation of the powdered activated carbon and reduce the cost.

When the functions of the musty odor estimating device 2 or the musty odor estimating device 2A described above are realized by software, the programs for realizing those functions are recorded in a computer-readable recording medium, and the programs are recorded. It may be read by a computer system and executed. It should be noted that the “computer system” mentioned here includes an OS (Operating System) and hardware such as 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 a computer system. .. Furthermore, "computer-readable recording medium" refers to volatile memory (RAM) inside a computer system that serves as 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 that hold the program for a certain period of time are also included.

Further, the above program may be transmitted from a computer system that stores 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 a program for realizing a part of the functions described above. Further, the above program may be a so-called difference file (difference program) that can realize the above-mentioned functions in combination with a program already recorded in the computer system.

Although some embodiments of the present invention have been described, these embodiments are presented as examples 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. The embodiments and their modifications are included in the scope of the invention and the scope thereof, and are included in the invention described in the claims and the scope of equivalents thereof.

1... Musty odor estimation system, 2 2A... Musty odor estimation device, 3... Display device, 4... Input device, 5... Raw water photographing device, 6... Cable, 13... Photographing tray, 15, 15a... Imaging unit, 16... Optical System, 17, 17a... Light source, 21... Image input section, 22... Wavelength band processing section, 23... Shape determination processing section, 24... Algae amount acquisition section, 25... Musty odor estimation table, 26, 26A... Input processing section, 27 , 27A... Musty odor estimation unit, 28... Display control unit, 29... Musty odor information storage unit, 31... Contaminant detection unit

Claims (9)

An image input unit for inputting image data of the 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 intensities of the plurality of wavelength bands in the image data input to the image input unit, a pixel area of the first algae contained in the raw water is extracted, and the extracted pixel area of the first algae is extracted. Based on, the algae amount acquisition unit for acquiring the amount of the first algae per unit amount of the raw water,
An offensive odor estimating unit that estimates the offensive odor degree of the raw water based on the amount of the first algae acquired by the algae amount acquiring unit,
On the basis of the signal intensity of the plurality of wavelength bands in the image data input to the image input unit, a contaminant detection unit that detects a pixel region of a contaminant contained in the raw water,
Offensive odor estimation device. The algae amount acquisition unit,
A wavelength band processing unit that extracts a second algae pixel region based on the signal intensities of the plurality of wavelength bands;
A shape determination unit that determines 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 off-flavor estimation apparatus according to claim 1 , further 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 wavelength band processing unit, based on the signal strength of the signal intensity of the first wavelength band and said second wavelength band, to claim 2 for extracting a pixel region of the second algae contained in the raw water The offensive odor estimation device described. The first wavelength band is a wavelength band of 650 nm to 700 nm,
The offensive odor estimation device according to claim 3 , wherein the second wavelength band is a wavelength band of 600 nm to 650 nm. The wavelength band processing unit sets the signal intensity of the first wavelength band output from the image capturing unit as A and the signal intensity of the second wavelength band as B, and sets arbitrary coefficients m, n (-1≦ m≦1, −1≦n≦1) is used to obtain the algal index indicating that the alga is the first alga: Algal index=(mA−nB)/(mA+nB)
The off-flavor estimation device according to claim 3 or 4 , wherein the pixel area of the second algae is extracted based on the acquired algae index. The second alga is cyanobacteria,
The offensive odor estimation device according to any one of claims 2 to 5 , wherein the first algae are cyanobacteria that cause the offensive odor. An imaging unit that photoelectrically converts light from the raw water that is the subject for each of a plurality of wavelength bands,
An offensive odor estimation device according to any one of claims 1 to 6 ,
An offensive odor estimation system. An image input step in which image data in which raw water is 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 intensities of the plurality of wavelength bands in the image data input in the image input step, the pixel area of the first algae contained in the raw water is extracted, and the extracted pixel area of the first algae is extracted. An algae amount acquisition step of acquiring the amount of the first algae per unit amount of the raw water based on
An off-odor estimating step of estimating the off-odor level of the raw water based on the amount of the first alga acquired in the alga-quantity acquiring step,
Based on the signal intensity of the plurality of wavelength bands in the image data input in the image input step, a contaminant detection step of detecting a pixel region of contaminants contained in the raw water,
A method for estimating an offensive odor. An image input step in which image data in which raw water is 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 intensities of the plurality of wavelength bands in the image data input in the image input step, the pixel area of the first algae contained in the raw water is extracted, and the extracted pixel area of the first algae is extracted. An algae amount acquisition step of acquiring the amount of the first algae per unit amount of the raw water based on
An off-odor estimating step of estimating the off-odor level of the raw water based on the amount of the first alga acquired in the alga-quantity acquiring step,
Based on the signal intensity of the plurality of wavelength bands in the image data input in the image input step, a contaminant detection step of detecting a pixel region of contaminants contained in the raw water,
A strange odor estimation program that causes a computer to execute.