KR20220097112A - Apparatus and method and for monitoring algae using in-situ optical particle size analyzer - Google Patents

Abstract

An apparatus and method for monitoring algae is disclosed. A device for monitoring algae according to an embodiment includes: a data acquisition part for acquiring particle size distribution data of floating particles in water measured at one or more points using an in-situ optical particle size analyzer; and a data analysis part for identifying a dominant species among a plurality of algae based on particle size distribution data and morphological characteristic data obtained in advance for each of the plurality of algae.

Description

Algae monitoring device and method using an optical particle size analyzer for field use

The disclosed embodiments relate to techniques for algae monitoring.

Recently, with the change of the river environment, water quality problems caused by algae are emerging. For this reason, the necessity of monitoring algae to identify the current status of algae and find solutions is emerging.

Existing algae monitoring methods are performed through intermittent sampling and population analysis through microscopic analysis. However, this method requires considerable effort and cost as it takes several days to several weeks or more from sampling to analysis, so there is a time and space limitation.

Republic of Korea Patent Publication No. 10-2019-0075696 (published on July 1, 2019)

Disclosed embodiments are intended to provide an apparatus and method for monitoring algae using an on-site optical particle size analyzer.

Algae monitoring device according to one embodiment, using an in-situ optical particle size analyzer (in-situ optical particle size analyzer) to obtain the particle size distribution data of suspended particles in water measured at one or more points (particle size) data acquisition unit; and a data analysis unit for identifying a dominant species among the plurality of algae based on the particle size distribution data and the morphological characteristic data obtained in advance for each of the plurality of algae.

The particle size distribution data may be data indicating the size and concentration of suspended particles in water.

The morphological characteristic data may include size data related to the morphological characteristics of each cell or colony of the plurality of algae.

When the dominant species is identified, the data analyzer may estimate the number of cells of the dominant species based on a predefined correlation between the concentration of the dominant species and the number of cells.

The data acquisition unit acquires particle size distribution data measured at each of a plurality of points having different at least one of a position on the water surface and a water depth, and the data analysis unit includes the particle size distribution data measured at each of the plurality of points and the shape Based on the characteristic data, a dominant species for each of the plurality of points among the plurality of birds may be identified.

The data analyzer may estimate the number of cells of the dominant species at each of the plurality of points based on a predefined correlation between the cell number and the concentration of the dominant species at each of the plurality of points.

Algae monitoring method according to an embodiment, using an in-situ optical particle size analyzer (in-situ optical particle size analyzer) to obtain particle size distribution data of suspended particles in water measured at one or more points step; and identifying a dominant species among the plurality of algae based on the particle size distribution data and the pre-obtained morphological characteristic data for each of the plurality of algae.

The particle size distribution data may be data indicating the size and concentration of suspended particles in water.

The morphological characteristic data may include size data related to the morphological characteristics of each cell or colony of the plurality of algae.

The algae monitoring method may further include, when the dominant species is identified, estimating the cell number of the dominant species based on a predefined correlation between the concentration and the cell number of the dominant species.

The acquiring may include acquiring particle size distribution data measured at each of a plurality of points having different positions on the water surface and at least one of a water depth, and the identifying may include: Based on the morphological characteristic data, a dominant species for each of the plurality of points among the plurality of birds may be identified.

The algae monitoring method may further include estimating the number of cells of the dominant species for each of the plurality of points based on a predefined correlation between the cell number and the concentration of the dominant species for each of the plurality of points. .

According to the disclosed embodiments, it is possible to quickly analyze algae at the measurement site using the particle size distribution data measured by the on-site optical particle size analyzer. It can reduce the cost and effort for

1 is a block diagram of an algae monitoring device according to an embodiment;
2 is an exemplary view for explaining a water surface measurement and a vertical measurement according to an embodiment;
3 to 6 are exemplary views for explaining the relationship between the morphological characteristic data of algal cells and the particle size distribution data.
7 and 8 are diagrams showing the results of experimentally confirming the particle size distribution data measured by the optical particle size analyzer for Hyunjeong at a specific point and the size of the dominant species of algae at the specific point;
9 is a flowchart of a bird monitoring method according to an embodiment;
10 is a flowchart of a bird monitoring method according to an additional embodiment;
11 is a block diagram illustrating and explaining a computing environment including a computing device according to an embodiment;

Hereinafter, specific embodiments of the present invention will be described with reference to the drawings. The following detailed description is provided to provide a comprehensive understanding of the methods, devices, and/or systems described herein. However, this is merely an example, and the present invention is not limited thereto.

In describing the embodiments of the present invention, if it is determined that the detailed description of the known technology related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted. And, the terms to be described later are terms defined in consideration of functions in the present invention, which may vary according to intentions or customs of users and operators. Therefore, the definition should be made based on the content throughout this specification. The terminology used in the detailed description is for the purpose of describing embodiments of the present invention only, and should in no way be limiting. Unless explicitly used otherwise, expressions in the singular include the meaning of the plural. In this description, expressions such as “comprising” or “comprising” are intended to indicate certain features, numbers, steps, acts, elements, some or a combination thereof, one or more other than those described. It should not be construed to exclude the presence or possibility of other features, numbers, steps, acts, elements, or any part or combination thereof.

1 is a block diagram of an algae monitoring apparatus according to an embodiment.

Referring to FIG. 1 , the bird monitoring apparatus 100 according to an embodiment includes a data acquisition unit 110 and a data analysis unit 120 .

According to an embodiment, the algae monitoring device 100 is based on particle size distribution data obtained through measurement of suspended particles in water using an in-situ optical particle size analyzer. It is a device for monitoring algae distributed in water.

According to an embodiment, the data acquisition unit 110 and the data analysis unit 120 are each implemented using one or more physically separated devices, or implemented by one or more hardware processors or a combination of one or more hardware processors and software. may be, and unlike the illustrated example, it may not be clearly distinguished in a specific operation.

The data acquisition unit 110 acquires particle size distribution data of suspended particles in the water measured at one or more points of the monitoring site by using an on-site optical particle size analyzer.

The on-site optical particle size analyzer refers to a device that can analyze the size and concentration of particles in the water at the monitoring site using diffraction of a laser. For example, the in-situ optical particle size analyzer may be, but not necessarily limited to, Sequoia Scientific's LISST (Laser In-Situ Scattering & Transmissometry)-100X, and it is based on laser diffraction to measure the concentration and size of particles in water at the monitoring site. A variety of known optical particle size analyzers configured to be able to analyze may be used for particle size distribution data acquisition.

According to an embodiment, the monitoring site may be, for example, internal water (內水, internal waters) in which algae can occur, such as a river, a lake, a reservoir, etc., but is not necessarily limited to a specific area if algae is possible.

On the other hand, the particle size distribution data means data indicating the size and concentration of suspended particles in water measured by an optical particle size analyzer for the field. According to an embodiment, the particle size distribution data may be data measured at a plurality of points in which at least one of a position on the water surface and a water depth is different by using an on-site optical particle size analyzer. To this end, the particle size distribution data may be obtained, for example, through at least one of a water surface measurement and a vertical measurement.

Specifically, FIG. 2 is an exemplary view for explaining a water surface measurement and a vertical measurement according to an embodiment.

2, the water surface measurement is, for example, after installing an optical particle size analyzer for the field on one side of a floating body (eg, a boat) 210, the floating body 210 is monitored at the site Particles suspended in water at a specific depth (e.g., 0.15 m from the water surface 200) of It can be carried out in a way that is measured.

According to an embodiment, when measuring the water surface, location data of a point where the measurement is made may be obtained along with the measurement of suspended particles in the water using an optical particle size analyzer for the field. In this case, the location data of the measurement point may be obtained using, for example, a GPS sensor provided in the floating body 210 , but the method of measuring the location data is not necessarily limited to a specific method.

The vertical measurement may be performed by measuring suspended particles in water while moving the optical particle size analyzer for the field in a vertical direction in the water while the floating body 210 is fixed at a specific position on the water surface 200 . As a specific example, the vertical measurement is performed after connecting the optical particle size analyzer for the field to the wire in a state in which the floating body 210 is fixed at a specific position on the water surface 200, and then the reel installed in the floating body 210. It can be performed in a way that measures suspended particles in water by descending to a specific water depth using

Meanwhile, the vertical measurement may be performed for one or more depths of water at one or more locations on the water surface 200 . In addition, in the case of vertical measurement according to an embodiment, position data and water depth data of a point at which the measurement is made may be obtained along with the measurement of suspended particles in the water using an optical particle size analyzer for the field. In this case, the position data may be obtained, for example, using a GPS sensor provided in the floating body 210, and the water depth data is, for example, attached to an optical particle size analyzer for the field or fixed to a wire together with an optical particle size analyzer for the field. It can be obtained using a depth sensor that descends through the reel.

Referring back to FIG. 1 , the data analysis unit 120 is configured to obtain a plurality of particles based on the particle size distribution data of suspended particles in the water obtained by the data acquisition unit 110 and the morphological characteristic data obtained in advance for each of the plurality of algae. Identify the dominant species among birds.

In this case, according to an embodiment, the morphological feature data of each of the plurality of algae may mean size data related to the morphological features of a cell or colony of each alga, and each pre-collected angle It can be derived in advance by observing the cells or colonies of algae through a microscope or the like.

Specifically, algae may have different sizes or different sizes of cells or colonies depending on the species, even if they are similar in shape. It may be size data that can indicate a difference due to the morphological characteristics of each bird, such as the range of diameter, horizontal length, and vertical length.

3 to 6 are exemplary views for explaining the relationship between morphological characteristic data and particle size distribution data of algal cells.

Specifically, in the case of algae having a rod-shaped cell having a horizontal length and a vertical length within a specific range, the horizontal or vertical length of the cell can be measured as a size by an optical particle size analyzer for the field as in the example shown in FIG. have.

Therefore, the particle size distribution data measured by the in-situ optical particle size analyzer at the point where the algae having the cell shape as shown in FIG. 3 is the dominant species is within the range of the horizontal length of the cells of the corresponding algae as in the example shown in FIG. The particle concentration of the corresponding particle size and the particle concentration of the corresponding particle size within the range of the longitudinal length of the cell of the corresponding algae will appear in the form of a double rod having a high value.

Accordingly, the data analysis unit 120 determines that the particle size distribution data of the suspended particles in the water obtained by the data acquisition unit 110 is a particle concentration of a particle size corresponding to the horizontal length range of a cell of a specific alga among a plurality of algae and the corresponding specific algae. In the case of a bimodal shape having a high value of particle size corresponding to the vertical length range of cells, the specific alga can be determined as the dominant species.

On the other hand, in the case of algae forming a colony having a diameter within a specific range, the diameter of the colony may be measured as a size by an optical particle size analyzer for the field as in the example shown in FIG. 5 .

Therefore, the particle size distribution data measured by the on-site optical particle size analyzer at the point where the algae having the form shown in FIG. 5 is the dominant species, as shown in the example shown in FIG. 6, is within the range of the colony diameter of the corresponding bird. The particle concentration of the particle size will appear in the form of a high value.

Accordingly, the data analysis unit 120 determines that the particle size distribution data of suspended particles in the water obtained by the data acquisition unit 110 has a high particle concentration at a particle size corresponding to the diameter range of a colony of a specific alga among a plurality of algae. In this case, the specific bird may be judged as the dominant species.

7 and 8 are diagrams showing the results of experimentally confirming particle size distribution data measured by an on-site optical particle size analyzer at a specific point and the size of the dominant species of algae at the specific point.

Specifically, in FIG. 7 (a), the cells of Aphanizomenon Flos-aquae, which are rod-shaped cells having a vertical length of about 80 μm and a horizontal length of about 5 μm, are predominantly Aphanizomenon Flos-aquae cells in FIG. It is a micrograph observed at 200 magnification, and (b) is a graph showing the particle size distribution data of suspended particles in water measured at the corresponding point using an on-site optical particle size analyzer.

Referring to the example shown in Figure 7, it can be seen that the particle concentration corresponding to each of the horizontal and vertical lengths of the cells of Aphanizomenon Flos-aquae in the graph shown in (b) has a high value, which (a) It can be seen that the horizontal and vertical lengths of the cells of Aphanizomenon Flos-aquae are consistent with the morphological characteristics shown in Fig. Accordingly, the data analysis unit 120 determines the dominant species at the point where the particle size distribution data is measured when the particle size distribution data of the suspended particles in the water obtained by the data acquisition unit 110 has a shape similar to that of (b) of FIG. 7 . Aphanizomenon Flos-aquae can be determined.

On the other hand, (a) of FIG. 8 is a micrograph of cells of Oscillatoria sancta, a type of blue-green algae having a transverse length of about 5 μm and a transverse length within the range of about 110 to 220 μm, observed at 200 magnification, and FIG. 8 (b) is a graph showing the particle size distribution data of suspended particles in water measured using a field optical particle size analyzer at the point where Oscillatoria sancta is the dominant species.

Referring to the example shown in Figure 8, it can be seen that the particle concentration corresponding to each of the horizontal and vertical lengths of Oscillatoria sancta cells in the graph shown in (b) has a high value, which is shown in (a). It can be seen that it is consistent with the morphological characteristics of the horizontal and vertical lengths of cells of Oscillatoria sancta. Therefore, the data analysis unit 120 determines the dominant species at the point where the particle size distribution data is measured when the particle size distribution data of the suspended particles in the water obtained by the data acquisition unit 110 has a shape similar to that of (b) of FIG. 8 . Oscillatoria sancta can be determined.

Referring back to FIG. 1 , when the dominant species is identified among the plurality of birds, the data analysis unit 120 determines the number of cells identified as the dominant species based on a predefined correlation between the concentration of the dominant species and the number of cells. The number of cells can be estimated.

In this case, according to one embodiment, the predefined correlation between the concentration of algae and the number of cells may be a relational expression obtained experimentally, and the concentration of the algae identified as the dominant species is a particle size based on the morphological characteristic data of the algae. It can be obtained from distribution data.

For example, assuming that the algae identified as the dominant species are algae with rod-shaped cells, the concentration of the algae is the range of the particle concentration corresponding to the horizontal length of the cell and the range of the vertical length of the cell in the particle size distribution data. may be the sum of particle concentrations corresponding to .

As another example, if it is assumed that the algae identified as the dominant species are algae having circular cells, the concentration of the algae may be the sum of particle concentrations corresponding to the range of diameters of the corresponding cells in the particle size distribution data.

On the other hand, according to an embodiment, when the particle size distribution data of the suspended particles in the water obtained by the data acquisition unit 110 is the particle size distribution data measured at each of a plurality of points in which at least one of a position on the water surface and a water depth is different, The data analysis unit 120 may identify a dominant species for each of the plurality of points among the plurality of birds based on the particle size distribution data measured at each of the plurality of points and the morphological characteristic data of each of the plurality of birds.

In addition, when the dominant species for each of the plurality of points is identified, the data analysis unit 120 determines the dominant species of each point based on a predefined correlation between the cell number and the concentration of algae identified as the dominant species for each point. The number of cells can be calculated.

On the other hand, according to one embodiment, the particle size distribution data of the suspended particles in the water obtained by the data acquisition unit 110 is the particle size distribution data measured at each of a plurality of points where at least one of a position on the water surface and a water depth is different, As described above, when location data and/or depth data of each measurement point are acquired, analysis of the spatial distribution of algae at the monitoring site is possible based on the dominant species identified at each measurement point and the concentration of the dominant species.

9 is a flowchart of a bird monitoring method according to an embodiment.

The method shown in FIG. 9 may be performed by the bird monitoring device 100 shown in FIG. 1 .

Referring to FIG. 9 , first, the algae monitoring apparatus 100 acquires particle size distribution data of suspended particles in water measured at one or more points using an on-site optical particle size analyzer ( 910 ).

In this case, according to an embodiment, the tidal current monitoring apparatus 110 may acquire particle size distribution data measured at each of a plurality of points where at least one of a position on the water surface and a water depth is different.

Meanwhile, the particle size distribution data may be data indicating the size and concentration of suspended particles in water measured by an on-site optical particle size analyzer.

Thereafter, the algae monitoring apparatus 100 identifies a dominant species among the plurality of algae based on the obtained particle size distribution data and the pre-obtained morphological characteristic data for each of the plurality of algae ( 920 ).

In this case, the morphological feature data may include size data related to the morphological features of cells or colonies of each of the plurality of algae.

Meanwhile, according to an exemplary embodiment, when the particle size distribution data measured at each of a plurality of points is obtained, the algae monitoring apparatus 100 performs a plurality of A dominant species for each of a plurality of points among birds may be identified.

10 is a flowchart of a bird monitoring method according to an additional embodiment.

The method shown in FIG. 10 may be performed by the bird monitoring device 100 shown in FIG. 1 .

Referring to FIG. 10 , first, the algae monitoring apparatus 100 acquires particle size distribution data of suspended particles in water measured at one or more points using an on-site optical particle size analyzer ( 1010 ).

In this case, according to an embodiment, the tidal current monitoring apparatus 110 may acquire particle size distribution data measured at each of a plurality of points where at least one of a position on the water surface and a water depth is different.

Meanwhile, the particle size distribution data may be data indicating the size and concentration of suspended particles in water measured by an on-site optical particle size analyzer.

Thereafter, the algae monitoring apparatus 100 identifies a dominant species among the plurality of algae based on the obtained particle size distribution data and pre-obtained morphological characteristic data for each of the plurality of algae ( 1020 ).

In this case, the morphological feature data may include size data related to the morphological features of cells or colonies of each of the plurality of algae.

Meanwhile, according to an exemplary embodiment, when the particle size distribution data measured at each of a plurality of points is obtained, the algae monitoring apparatus 100 performs a plurality of A dominant species for each of a plurality of points among birds may be identified.

Thereafter, the algae monitoring apparatus 100 estimates the number of cells of the dominant species based on a predefined correlation between the identified concentration of the dominant species and the number of cells ( 1030 ).

At this time, according to an embodiment, when the dominant species for each of the plurality of points is identified, the dominant species for each of the plurality of points is based on the predefined correlation between the concentration of the dominant species for each of the plurality of points and the number of cells. The number of cells can be estimated.

On the other hand, at least some of the steps in the flowcharts shown in FIGS. 9 and 10 are performed in a reversed order, performed together in combination with other steps, omitted, divided into detailed steps, or one or more steps not shown may be added and performed.

11 is a block diagram illustrating and explaining a computing environment including a computing device according to an embodiment. In the illustrated embodiment, each component may have different functions and capabilities other than those described below, and may include additional components in addition to those described below.

The illustrated computing environment 110 includes a computing device 12 . In one embodiment, computing device 12 may be one or more components included in bird monitoring device 100 shown in FIG. 1 .

Computing device 12 includes at least one processor 14 , computer readable storage medium 16 , and communication bus 18 . The processor 14 may cause the computing device 12 to operate in accordance with the exemplary embodiments discussed above. For example, the processor 14 may execute one or more programs stored in the computer-readable storage medium 16 . The one or more programs may include one or more computer-executable instructions that, when executed by the processor 14, configure the computing device 12 to perform operations in accordance with the exemplary embodiment. can be

Computer-readable storage medium 16 is configured to store computer-executable instructions or program code, program data, and/or other suitable form of information. The program 20 stored in the computer-readable storage medium 16 includes a set of instructions executable by the processor 14 . In one embodiment, computer-readable storage medium 16 includes memory (volatile memory, such as random access memory, non-volatile memory, or a suitable combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash It may be memory devices, other forms of storage medium accessed by computing device 12 and capable of storing desired information, or a suitable combination thereof.

Communication bus 18 interconnects various other components of computing device 12 , including processor 14 and computer readable storage medium 16 .

Computing device 12 may also include one or more input/output interfaces 22 and one or more network communication interfaces 26 that provide interfaces for one or more input/output devices 24 . The input/output interface 22 and the network communication interface 26 are coupled to the communication bus 18 . Input/output device 24 may be coupled to other components of computing device 12 via input/output interface 22 . Exemplary input/output device 24 may include a pointing device (such as a mouse or trackpad), a keyboard, a touch input device (such as a touchpad or touchscreen), a voice or sound input device, various types of sensor devices, and/or imaging devices. input devices, and/or output devices such as display devices, printers, speakers and/or network cards. The exemplary input/output device 24 may be included in the computing device 12 as a component constituting the computing device 12 , and may be connected to the computing device 12 as a separate device distinct from the computing device 12 . may be

Although the present invention has been described in detail through representative embodiments above, those of ordinary skill in the art to which the present invention pertains can make various modifications to the above-described embodiments without departing from the scope of the present invention. will understand Therefore, the scope of the present invention should not be limited to the described embodiments, but should be defined by the claims described below as well as the claims and equivalents.

10: Computing Environment
12: computing device
14: Processor
16: computer readable storage medium
18: communication bus
20: Program
22: input/output interface
24: input/output device
26: network communication interface
100: bird monitoring device
110: data acquisition unit
120: data analysis unit

Claims (12)

a data acquisition unit for acquiring particle size distribution data of suspended particles measured at one or more points using an in-situ optical particle size analyzer; and
Based on the particle size distribution data and the morphological characteristic data obtained in advance for each of the plurality of algae, comprising a data analysis unit for identifying a dominant species among the plurality of algae, algae monitoring device.
The method according to claim 1,
The particle size distribution data is data indicating the size and concentration of suspended particles in the water, algae monitoring device.
The method according to claim 1,
The morphological characteristic data includes size data related to the morphological characteristics of each cell or colony of the plurality of algae, algae monitoring device.
The method according to claim 1,
The data analysis unit, when the dominant species is identified, estimating the number of cells of the dominant species based on a predefined correlation between the concentration and the cell number of the dominant species, algae monitoring device.
The method according to claim 1,
The data acquisition unit acquires particle size distribution data measured at each of a plurality of points where at least one of a position on the water surface and a water depth is different,
The data analysis unit, based on the particle size distribution data and the morphological characteristic data measured at each of the plurality of points, to identify a dominant species for each of the plurality of points among the plurality of birds, a bird monitoring device.
6. The method of claim 5,
The data analysis unit, algae monitoring device for estimating the number of cells of the dominant species for each of the plurality of points based on a predefined correlation between the concentration and the number of cells of the dominant species for each of the plurality of points.
acquiring particle size distribution data of suspended particles measured at one or more points using an in-situ optical particle size analyzer; and
and identifying a dominant species among the plurality of algae based on the particle size distribution data and pre-obtained morphological characteristic data for each of the plurality of algae.
8. The method of claim 7,
The particle size distribution data is data representing the size and concentration of suspended particles in the water, the algae monitoring method.
8. The method of claim 7,
The morphological characteristic data includes size data related to the morphological characteristics of each cell or colony of the plurality of algae, algae monitoring method.
8. The method of claim 7,
when the dominant species is identified, estimating the cell number of the dominant species based on a predefined correlation between the concentration of the dominant species and the cell number.
8. The method of claim 7,
The acquiring includes acquiring particle size distribution data measured at each of a plurality of points where at least one of a position on the water surface and a water depth is different,
The identifying may include identifying a dominant species for each of the plurality of points among the plurality of birds based on the particle size distribution data and the morphological characteristic data measured at each of the plurality of points.
12. The method of claim 11,
estimating the cell number of the dominant species for each of the plurality of points based on a predefined correlation between the cell number and the concentration of the dominant species for each of the plurality of points.

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