JPH06303965A - Microorganism recognition apparatus - Google Patents

Abstract

(57) [Summary] [Structure] The video signal picked up by the image pickup device 200 is input to the on-site monitor 340 and the image processing device 310, where image processing is performed, and the size, number, brightness, etc. of the filamentous microorganisms. Is calculated, the state of the microorganism is determined based on these calculated values, and an abnormal signal is transmitted to the control circuit. [Effect] According to the present invention, by measuring the brightness of filamentous microorganisms, the brightness is divided into levels, and the amount of filamentous fungi at each level is displayed, whereby the amount of malignant filamentous fungi that causes bulking is displayed. It will be possible to improve the prediction of bulking.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a microorganism recognition device for monitoring microorganisms in a sewage treatment process or the like.

[0002]

2. Description of the Related Art In a sewage treatment plant, microorganisms are allowed to ingest organic matter in the inflowing water in an aeration tank, and the microorganisms are settled in a sedimentation tank. In order to improve the quality of water discharged from sewage treatment plants, it is necessary to improve the shape of microorganisms. Microorganisms are roughly classified into aggregating microorganisms and filamentous microorganisms, but if filamentous microorganisms proliferate too much, sedimentation becomes poor.

The deterioration of the sedimentation property is called the bulking phenomenon. When the settling property deteriorates, microorganisms flow out from the settling basin, resulting in deterioration of discharged water quality. Therefore, it is important not to propagate filamentous microorganisms. Examples of filamentous microorganisms include Sphaeroterus. In order to prevent the filamentous microorganisms from overpropagating, it is necessary to continuously and quantitatively measure the types of microorganisms and their appearance amounts or concentrations and reflect them in operation management.
In this case, without disturbing the state of aggregation of microorganisms and their habitat environment,
It is important to get an accurate microbial status.

Conventionally, in order to monitor the state of microorganisms, there has been proposed a microorganism recognizing device which images the microorganisms by an image pickup means and utilizes an image processing technique. Specifically, the image signal (grayscale image) picked up by the image pickup means is binarized by a threshold value, and then the microorganisms are extracted and recognized, and quantitatively measured. On the other hand, since microorganisms and objects in water are constantly changing, continuous measurement of microorganisms is desired. A microorganism recognition apparatus utilizing image processing technology is disclosed in, for example, Japanese Patent Laid-Open No. 62-50
It is described in Japanese Patent No. 608. This is a measuring device that can always measure the length of filamentous microorganisms accurately regardless of its thickness. It measures only the length of filamentous microorganisms, not the characteristics of filamentous microorganisms. It is generally considered that filamentous microorganisms are more likely to cause bulking when the brightness thereof is darker in a captured grayscale image. Based on this, a measuring device for filamentous microorganisms based on brightness or the like is required.

[0005]

The filamentous microorganism is more likely to cause bulking as the brightness of the grayscale image of the filamentous microorganism is darker. By measuring the brightness of filamentous microorganisms,
For example, an increase in the amount of dark filamentous microorganisms can help identify signs of bulking.

[0006]

SUMMARY OF THE INVENTION An object of the present invention is to provide a microorganism comprising an image pickup device for picking up an image of microorganisms in a liquid, an image processing device for processing an image based on a video signal thereof, and a monitor device for displaying this image. In the recognition device, a binarized image obtained by binarizing the video signal is obtained, and the binarized image of the filamentous microorganism and the grayscale image are ANDed to classify the intensity of the luminance of the filamentous microorganism into levels. Correlate it with the quantity,
Compare the amount of each level.

[0007]

By analyzing the brightness of the filamentous microorganisms, it is possible to clearly inform the operator of the increase in the malignant filamentous microorganisms that cause bulking, and it is possible to improve the prediction of bulking.

[0008]

[Example] In FIG.
At 0, large dust or the like is settled and removed, and then flows into the aeration tank 110. Further, return sludge (microorganisms) is supplied from the final settling tank 150 to the aeration tank 110 via the sludge return pipe 160. The air sent from the blower 140 via the air pipe 130 is diffused into the aeration tank 110 by the diffuser 120. Thereby, the aeration tank 11
The returned sludge and wastewater supplied to the inside of 0 are mixed by stirring. The returned sludge, that is, activated sludge, has a particle size of 0.
It is a mass (flock) of about 1 to 1.0 mm and contains several tens of microorganisms, but when roughly classified, it consists of a flocculating microorganism and a filamentous microorganism. Activated sludge absorbs oxygen in the supplied air and decomposes organic matter in wastewater into carbon dioxide and water. A part of the organic matter is applied to the bacterial growth of the activated sludge. The mixed liquid of activated sludge and sewage is guided to the final settling tank 150, where the activated sludge settles by gravity. The supernatant is usually discharged after chlorine sterilization. On the other hand, the sludge return pipe 160
Is returned to the aeration tank 110 as return sludge.

The image pickup device 200 is an aeration tank 1.
It is immersed in 10 liquids and images microorganisms in the aeration tank. The video signal (grayscale image) imaged by the imaging device 200 is input to the on-site monitor 340 via the on-site operation panel 330 and the central operation panel 30.
It is input to the image processing device 310 via 0. The site control panel 330 supplies power to the imaging device 200 and
A television camera installed in the imaging device 200, lighting,
An operation signal for cleaning and sampling (on-site operation panel) is given, and a similar control signal (central operation panel) from the central operation panel 300 is also transmitted via the on-site operation panel 330.
Give to 00. The on-site monitor 340 is always the imaging device 2
The enlarged image of the microorganism from 00 is displayed. When operating the on-site operation panel 330, the operation is performed while referring to the microorganism enlarged image on the on-site monitor 340. This operation includes, for example, adjustment of illumination intensity.

The central operation panel 300 receives the video signal transmitted from the site operation panel 330, and the image processing device 31
0, and a device for transmitting a control signal for remotely operating the imaging device 200 to the site operation panel 330. The image processing device 310 performs image processing on the image signal of the enlarged microorganism image from the developing device 200 to identify the microorganism,
The size, number, appearance frequency, etc. of the microorganisms are calculated, the microorganisms are classified based on these calculated values, and the processed image and the calculated values obtained in each processing process are displayed on the central monitor 320. Then, the abnormal signal is transmitted to the control circuit 500.

The control circuit 500 controls factors that influence the reproduction of filamentous microorganisms based on the abnormal signal transmitted from the image processing device 310 and the measured value of the water quality measuring means 510.
This factor is, for example, organic matter load, DO, PH, N and P.
Balance, amount of returned sludge, water temperature, amount of excess sludge, amount of inflow water, sludge retention time, etc. The control method is, for example, low DO.
For bulking, which is considered to be caused by the above, adjust the air flow rate, and for bulking due to low organic matter load, adjust the mixing ratio of return sludge and inflow water.

The water quality measuring means 510 includes DO, MLS
There are measuring functions such as S, PH, alkalinity, and flow rate, and those measured values are transmitted to the control circuit 500. The central monitor 320 displays the original image and the processed image transmitted from the image processing device 310, and the calculated value.

FIG. 2 is a diagram showing the structure of the image processing apparatus 310.

The image signal from the image pickup device 200 is processed by the image processing processor 350 of the image processing unit 370, the identification of microorganisms, the size, the number of microorganisms, the appearance frequency, the brightness, etc. of the microorganisms are calculated, and these calculated values are calculated by the system. The data is transmitted to the central processing unit 520 via the bus 507. Image processing memory 360
Stores the grayscale image from the imaging device 200, and also stores the processed image obtained from each processing step of the image processing processor 350. The central processing unit 520 diagnoses the microbial state based on the calculated value output from the image processor 350. Information necessary for these processes, programs, data, and the like are stored in the main memory 600, the auxiliary memory 601, or the disk 602.

A video signal of filamentous microorganisms picked up by an image pickup device is subjected to noise processing from the binarized image of Fig. 3.1 (grayscale image) to obtain a binary image of only filamentous microorganisms (Fig. 3). .2). AN of this binarized image and grayscale image
Taking D gives a grayscale image of only filamentous microorganisms. The brightness is taken for each dot from this image, and the brightness is divided into several levels according to the strength of the brightness (FIG. 4).

By correlating the brightness divided into the levels with the amount of the filamentous fungus, it can be said that, for example, if the amount of the filamentous fungus at a dark level is large, it tends to cause bulking. Therefore, an abnormal signal is transmitted to the control circuit. . The abnormal signal is, for example, a sign of bulking.

Therefore, in the past, bulking was predicted only by the amount of filamentous fungi. This time, the property of the filamentous fungus is shown by measuring the brightness, and the amount of malignant (prone to bulking) filamentous fungus is investigated. The bulking can be predicted more accurately than the conventional measurement.

[0018]

According to the present invention, the brightness of filamentous microorganisms is measured to classify the brightness levels, and the amount of the filamentous fungi at each level is displayed to display the amount of the filamentous fungi. The amount of (darker filamentous fungi) can be known, which can improve the prediction of bulking.

[Brief description of drawings]

FIG. 1 is a diagram showing an image monitoring system showing an embodiment of the present invention.

FIG. 2 is a configuration diagram of an image processing apparatus.

FIG. 3 is a diagram showing a binary image, a grayscale image, and an image obtained by ANDing them.

FIG. 4 is a diagram showing level division of luminance of filamentous fungi.

[Explanation of symbols]

110 ... Aeration tank, 200 ... Imaging device, 3
00 ... Central operation panel, 310 ... Image processing device, 320 ... Monitor, 330 ... On-site operation panel, 350 ... Image processing processor, 360 ... Image processing memory, 370 ... Image processing unit, 5
00 ... Control circuit.

 ─────────────────────────────────────────────────── ─── Continuation of the front page (72) Inventor Shoji Watanabe 7-1-1 Omika-cho, Hitachi-shi, Ibaraki Hitachi Ltd. Hitachi Research Laboratory

Claims (1)

[Claims] 1. A microbial recognition device comprising an image pickup device for picking up images of microorganisms in a liquid, an image processing device for image processing based on the image signal, and a monitor device for displaying the image, wherein the image signal is binary. The binarized image that has been subjected to the binarization process is obtained, and the brightness of the filamentous microorganism is measured from the AND of the grayscale image and this binarized image, and the intensity of the filamentous microorganism is measured in several steps. A microbial recognition device characterized by measuring the amount of filamentous fungi and the properties of the filamentous fungus by measuring the brightness by classifying the levels into levels and corresponding the levels.