CN116503650A - Water body blue algae monitoring system and device - Google Patents

Water body blue algae monitoring system and device Download PDF

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Publication number
CN116503650A
CN116503650A CN202310445529.3A CN202310445529A CN116503650A CN 116503650 A CN116503650 A CN 116503650A CN 202310445529 A CN202310445529 A CN 202310445529A CN 116503650 A CN116503650 A CN 116503650A
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blue algae
water body
image
water
pixel
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董斌
时昌花
高祥
徐志立
邹秋月
王露露
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Anhui Agricultural University AHAU
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Abstract

The invention discloses a water body blue algae monitoring system and a device, which belong to the technical field of water body detection and specifically comprise the following steps: the map library is used for storing images of each blue algae growth stage; the data acquisition module is used for acquiring the water body image in real time, generating a water body image set, acquiring the water body sample in real time and detecting water body sample data; the image processing module is used for constructing a blue algae growth recognition model based on the map library, inputting the water body image into the blue algae growth recognition model and recognizing the growth stage of blue algae in the water body image set; the data optimization module is used for extracting a water body image which cannot be identified by the blue algae identification model, acquiring a corresponding water body sample, collecting a close-range image of the water body sample, and judging whether blue algae exists in the water body according to the close-range image; the blue algae monitoring system is based on the image to be detected of the water surface, automatically monitors the blue algae in real time, and performs early warning according to the burst intensity result of the blue algae.

Description

Water body blue algae monitoring system and device
Technical Field
The invention relates to the technical field of water body monitoring, in particular to a water body blue algae monitoring system and device.
Background
Blue algae or blue-green algae is covered on the surface of a water body to present different colors such as blue, blue-green and the like, belongs to prokaryotic microorganisms, and when a large amount of substances containing nitrogen and phosphorus are discharged into the water body, the water body is eutrophicated to promote the overgrowth of cyanobacteria, the cyanobacteria covers the water surface to cause the water body to present the phenomenon of different colors, and according to statistics, the cyanobacteria bloom can be prevented, and a certain engineering measure can be adopted before the bloom outbreak, so that the cyanobacteria bloom can be prevented or the damage can be reduced.
There are four main types of methods for blue algae warning: firstly satellite remote sensing monitoring, secondly manual monitoring, thirdly unmanned aerial vehicle detection and thirdly underwater automatic monitoring. However, these four types of methods have disadvantages, such as: satellite remote sensing monitoring is affected by weather, and is often unable to monitor due to the fact that a water area is covered by cloud layers; the area of the water area covered by manual monitoring and unmanned aerial vehicle monitoring is small; the underwater automatic monitoring method has higher cost and general effect.
In summary, the existing methods cannot provide autonomous, real-time and accurate blue algae monitoring and early warning functions at the same time. Aiming at the defects of the existing method, the invention provides a water body blue algae monitoring system, realizes autonomous and real-time monitoring of blue algae, accurately evaluates the burst intensity of blue algae, and improves the level of blue algae monitoring, early warning and treatment.
Disclosure of Invention
The invention aims to provide a water body blue algae monitoring system and device, which solve the following technical problems:
aiming at the defects of the existing method, the autonomous and real-time monitoring of the blue algae is realized, the burst intensity of the blue algae is accurately estimated, and the monitoring, early warning and treatment levels of the blue algae are improved.
The aim of the invention can be achieved by the following technical scheme:
a water body cyanobacteria monitoring system, comprising:
the map library is used for storing images of each blue algae growth stage;
the data acquisition module is used for acquiring the water body image in real time, generating a water body image set, acquiring the water body sample in real time and detecting water body sample data;
the image processing module is used for constructing a blue algae growth recognition model based on the map library, inputting the water body image into the blue algae growth recognition model and recognizing the growth stage of blue algae in the water body image set;
the data optimization module is used for extracting a water body image which cannot be identified by the blue algae identification model, acquiring a corresponding water body sample, collecting a close-range image of the water body sample, and judging whether blue algae exists in the water body according to the close-range image.
As a further scheme of the invention: the process of judging whether blue algae exists in the water body by the data optimization module is as follows:
carrying out gray level processing on the near-field image to obtain a gray level image, carrying out noise reduction processing on the gray level image, carrying out edge detection on the gray level image after noise reduction by adopting a Sobel edge detection operator, carrying out binarization processing on the gray level image after edge detection by adopting a maximum inter-class variance method to obtain a binarized image, wherein the pixel value of the gray level image after binarization processing is 0 or 255, and carrying out noise reduction processing on the binarized image again;
as a further scheme of the invention: the process of judging whether blue algae exists in the water body by the data optimization module further comprises the following steps: a step of
Selecting the outline of a white communication area in a binary image through a preset selecting frame to obtain a to-be-calibrated selecting area, respectively calculating the pixel ratio of 255 gray values and 0 gray values in the to-be-calibrated selecting area and the jump frequency range of gray values in the to-be-calibrated selecting area if a plurality of to-be-calibrated selecting areas exist, and judging the to-be-calibrated selecting area as a blue algae image if the pixel ratio of the to-be-calibrated selecting area is lower than 0.25 and the jump frequency range is in [5,30], otherwise, continuing selecting.
As a further scheme of the invention: the process of constructing the blue algae growth recognition model by the image processing module comprises the following steps:
constructing a specimen training data set based on blue algae pixel characteristic data and non-blue algae pixel characteristic data of images in the map library; training a classification algorithm model based on the specimen training data set to obtain a blue algae growth recognition model.
As a further scheme of the invention: the blue algae growth recognition model comprises the following recognition processes:
classifying the characteristic data of the image pixels in the water body image set based on the blue algae growth recognition model to obtain a blue algae pixel characteristic data set and a non-blue algae pixel characteristic data set; and calculating the threshold value of each pixel component by using an optimization algorithm based on the blue algae pixel characteristic data set and the non-blue algae pixel characteristic data set as the blue algae pixel characteristic threshold value, and comparing the blue algae pixel characteristic threshold value with a preset threshold value to obtain a blue algae growth stage.
As a further scheme of the invention: further comprising a pretreatment step comprising:
detecting the brightness of the water body image, and removing the water body image which does not reach the preset brightness threshold; determining a selected region in the water body image; in the preprocessing step, when the ratio of the area of the blue algae area to the area of the selected area of the water body image exceeds an early warning threshold value, early warning information is generated.
As a further scheme of the invention: the system also comprises an early warning module, wherein the working process of the early warning module is as follows:
measuring the nitrogen concentration of the water body sample, marking the nitrogen concentration as D, measuring the phosphorus concentration as L, measuring the water body temperature as T, calculating the water body eutrophication degree F according to a water body eutrophication formula F=Da+Lb+Tc, and sending out early warning information when the water body eutrophication degree is larger than a preset threshold value.
The utility model provides a water blue algae monitoring devices for foretell water blue algae monitoring system, includes the base, base top fixedly connected with water pump, the inlet end intercommunication of water pump has sampling probe, sampling probe is used for gathering the water sample, the water pump periphery is provided with the protective housing, the protective housing with base fixed connection, water outlet end one side of water pump is provided with the observation dish, the observation dish top is provided with first camera, first camera with protective housing fixed connection, protective housing top fixedly connected with support, the support top rotates and is connected with the second camera.
The invention has the beneficial effects that:
the blue algae monitoring system is based on the image to be detected of the water surface, automatically monitors the blue algae in real time, and performs early warning according to the burst intensity result of the blue algae. The non-contact image acquisition mode is adopted, so that a water sample is not required to be acquired in the field, and the maintenance cost of acquisition equipment is low; the image acquisition, the processing and the outbreak intensity calculation to be detected are realized on the monitoring device, the outbreak intensity result is only transmitted to the remote monitoring center, the data transmission quantity is reduced, and the network communication cost is saved; and the early warning information and the corresponding blue algae positioning information are sent to a remote monitoring center together, so that the blue algae outbreak position can be positioned conveniently, and basic data is provided for the resource scheduling of blue algae salvage.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a schematic diagram of a water body blue algae monitoring device;
FIG. 2 is a schematic flow chart of a water body blue algae monitoring system of the invention.
In the figure: 1. a base; 2. a protective shell; 3. a sampling probe; 4. a water pump; 5. an observation dish; 6. a first camera; 7, a second camera; 8. and (3) a bracket.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention is a water body blue algae monitoring system, comprising:
the map library is used for storing images of each blue algae growth stage;
the data acquisition module is used for acquiring the water body image in real time, generating a water body image set, acquiring the water body sample in real time and detecting water body sample data;
the image processing module is used for constructing a blue algae growth recognition model based on the map library, inputting the water body image into the blue algae growth recognition model and recognizing the growth stage of blue algae in the water body image set;
the data optimization module is used for extracting a water body image which cannot be identified by the blue algae identification model, acquiring a corresponding water body sample, collecting a close-range image of the water body sample, and judging whether blue algae exists in the water body according to the close-range image.
In a preferred embodiment of the present invention, the process of determining whether blue algae exists in the water body by the data optimization module is:
carrying out gray level processing on the near-field image to obtain a gray level image, carrying out noise reduction processing on the gray level image, carrying out edge detection on the gray level image after noise reduction by adopting a Sobel edge detection operator, carrying out binarization processing on the gray level image after edge detection by adopting a maximum inter-class variance method to obtain a binarized image, wherein the pixel value of the gray level image after binarization processing is 0 or 255, and carrying out noise reduction processing on the binarized image again;
in a preferred case of this embodiment, the process of determining whether blue algae exists in the water body by the data optimization module further includes:
selecting the outline of a white communication area in a binary image through a preset selecting frame to obtain a to-be-calibrated selecting area, respectively calculating the pixel ratio of 255 gray values and 0 gray values in the to-be-calibrated selecting area and the jump frequency range of gray values in the to-be-calibrated selecting area if a plurality of to-be-calibrated selecting areas exist, judging the to-be-calibrated selecting area as a blue algae image if the pixel ratio of the to-be-calibrated selecting area is lower than 0.25 and the jump frequency range is in [5,30], otherwise, continuously selecting
In another preferred embodiment of the present invention, the process of constructing the blue algae growth identification model by the image processing module is as follows:
constructing a specimen training data set based on blue algae pixel characteristic data and non-blue algae pixel characteristic data of a sample image in the map library; training a classification algorithm model based on the specimen training data set to obtain a blue algae growth recognition model;
and establishing a blue algae growth recognition model to recognize the growth stage of blue algae through a sample image in the map library, and improving the accuracy of model recognition based on big data.
In a preferred case of this embodiment, the identification process of the blue algae growth identification model is:
classifying the characteristic data of the image pixels in the water body image set based on the blue algae growth recognition model to obtain a blue algae pixel characteristic data set and a non-blue algae pixel characteristic data set; calculating threshold values of pixel components by using an optimization algorithm based on the blue algae pixel characteristic data set and the non-blue algae pixel characteristic data set as the blue algae pixel characteristic threshold value, comparing the blue algae pixel characteristic threshold value with a preset threshold value, and judging a blue algae growth stage;
threshold component calculation is carried out on blue algae water body pixel characteristics and non-blue algae water body pixel characteristics, and each water body image extraction pixel belongs to a blue algae area or a non-blue algae area, so that blue algae pixel characteristic data of the blue algae area is obtained, non-blue algae pixel characteristic data of the non-blue algae area is distributed with class labels, each extracted pixel data forms a piece of pixel characteristic training data, and the content of the pixel characteristic training data comprises: category labels, pixel feature data; the class labels are blue algae pixels or non-blue algae pixels, the pixel characteristic data are pixel characteristic values such as RGB, and the like, and the operation is carried out on each selected water body image.
In another preferred case of this embodiment, the optimization algorithm formula is:
wherein X is a pixel component, T X Is the threshold value of the pixel component X, X z Is the pixel component, X of blue algae pixel characteristic data nz I is the serial number of the blue algae pixel characteristic data in the blue algae pixel characteristic data set, j is the serial number of the non-blue algae pixel characteristic data in the non-blue algae pixel characteristic data set, and N is the pixel component of the non-blue algae pixel characteristic data z N is the number of blue algae pixel characteristic data in the blue algae pixel characteristic data set nz For the number of non-blue algae pixel characteristic data in the non-blue algae pixel characteristic data set, min () and max () represent the minimum value and the maximum value of the pixel component X respectively.
In another preferred case of the present embodiment, the method further includes a pretreatment step including:
detecting the brightness of the water body image, and removing the water body image which does not reach the preset brightness threshold; determining a selected region in the water body image; in the preprocessing step, when the ratio of the area of the blue algae area to the area of the selected area of the water body image exceeds an early warning threshold value, early warning information is generated.
In another preferable case of this embodiment, the positioning information of the position corresponding to the water body image is detected, and the positioning information and the early warning information are sent to a remote monitoring center, or the ratio of the area of the blue algae area to the area of the water body image, the positioning information and the early warning information are sent to the remote monitoring center.
In another preferable case of this embodiment, the process of detecting the water body sample parameter by the water body sampling module is:
measuring the nitrogen concentration of the water body sample, marking the nitrogen concentration as D, measuring the phosphorus concentration as L, measuring the water body temperature as T, calculating the water body eutrophication degree F according to a water body eutrophication formula F=Da+Lb+Tc, and sending out early warning information when the water body eutrophication degree is larger than a preset threshold value;
a water blue algae monitoring devices for above-mentioned water blue algae monitoring system, including base 1, base 1 top fixedly connected with water pump 4, the end intercommunication that intakes of water pump 4 has sampling probe 3, sampling probe 3 is used for gathering the water sample, water pump 4 periphery is provided with protective housing 2, protective housing 2 with base 1 fixed connection, water outlet end one side of water pump 4 is provided with observation dish 5, observation dish 5 top is provided with first camera 6, first camera 6 with protective housing 2 fixed connection, protective housing 2 top fixedly connected with support 8, support 8 top rotation is connected with second camera 7.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.

Claims (8)

1. A water body cyanobacteria monitoring system, comprising:
the map library is used for storing images of each blue algae growth stage;
the data acquisition module is used for acquiring the water body image in real time, generating a water body image set, acquiring the water body sample in real time and detecting water body sample data;
the image processing module is used for constructing a blue algae growth recognition model based on the map library, inputting the water body image into the blue algae growth recognition model and recognizing the growth stage of blue algae in the water body image set;
the data optimization module is used for extracting a water body image which cannot be identified by the blue algae identification model, acquiring a corresponding water body sample, collecting a close-range image of the water body sample, and judging whether blue algae exists in the water body according to the close-range image.
2. The system for monitoring blue algae in water according to claim 1, wherein the process of judging whether blue algae exists in the water by the data optimization module is as follows:
and carrying out gray level processing on the near-field image to obtain a gray level image, carrying out noise reduction processing on the gray level image, carrying out edge detection on the gray level image after noise reduction by adopting a Sobel edge detection operator, carrying out binarization processing on the gray level image after edge detection by adopting a maximum inter-class variance method to obtain a binarized image, wherein the pixel value of the gray level image after binarization processing is 0 or 255, and carrying out noise reduction processing on the binarized image again.
3. The system of claim 2, wherein the process of determining whether blue algae is present in the water by the data optimization module further comprises: a step of
Selecting the outline of a white communication area in a binary image through a preset selecting frame to obtain a to-be-calibrated selecting area, respectively calculating the pixel ratio of 255 gray values and 0 gray values in the to-be-calibrated selecting area and the jump frequency range of gray values in the to-be-calibrated selecting area if a plurality of to-be-calibrated selecting areas exist, and judging the to-be-calibrated selecting area as a blue algae image if the pixel ratio of the to-be-calibrated selecting area is lower than 0.25 and the jump frequency range is in [5,30], otherwise, continuing selecting.
4. The water body blue algae monitoring system according to claim 1, wherein the image processing module constructs a blue algae growth recognition model by the following steps:
constructing a specimen training data set based on blue algae pixel characteristic data and non-blue algae pixel characteristic data of images in the map library; training a classification algorithm model based on the specimen training data set to obtain a blue algae growth recognition model.
5. The system for monitoring blue algae in water according to claim 4, wherein the identification process of the blue algae growth identification model is as follows:
classifying the characteristic data of the image pixels in the water body image set based on the blue algae growth recognition model to obtain a blue algae pixel characteristic data set and a non-blue algae pixel characteristic data set; and calculating the threshold value of each pixel component by using an optimization algorithm based on the blue algae pixel characteristic data set and the non-blue algae pixel characteristic data set as the blue algae pixel characteristic threshold value, and comparing the blue algae pixel characteristic threshold value with a preset threshold value to obtain a blue algae growth stage.
6. The water body cyanobacteria monitoring system of claim 1, further comprising a pretreatment step comprising:
detecting the brightness of the water body image, and removing the water body image which does not reach the preset brightness threshold; determining a selected region in the water body image; in the preprocessing step, when the ratio of the area of the blue algae area to the area of the selected area of the water body image exceeds an early warning threshold value, early warning information is generated.
7. The water body blue algae monitoring system according to claim 1, further comprising an early warning module, wherein the working process of the early warning module is as follows:
measuring the nitrogen concentration of the water body sample, marking the nitrogen concentration as D, measuring the phosphorus concentration as L, measuring the water body temperature as T, calculating the water body eutrophication degree F according to a water body eutrophication formula F=Da+Lb+Tc, and sending out early warning information when the water body eutrophication degree is larger than a preset threshold value.
8. The utility model provides a water blue algae monitoring devices for implementing a water blue algae monitoring system of claim 1, includes base (1), its characterized in that, base (1) top fixedly connected with water pump (4), the inlet end intercommunication of water pump (4) has sampling probe (3), sampling probe (3) are used for gathering the water sample, water pump (4) periphery is provided with protective housing (2), protective housing (2) with base (1) fixed connection, water outlet end one side of water pump (4) is provided with observation dish (5), observation dish (5) top is provided with first camera (6), first camera (6) with protective housing (2) fixed connection, protective housing (2) top fixedly connected with support (8), support (8) top rotation is connected with second camera (7).
CN202310445529.3A 2023-04-21 2023-04-21 Water body blue algae monitoring system and device Pending CN116503650A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117092100A (en) * 2023-08-22 2023-11-21 华南理工大学 Image processing-based intermittent sampling detection method and related device for water environment
CN117890258A (en) * 2024-03-15 2024-04-16 水利部交通运输部国家能源局南京水利科学研究院 Non-contact visual algae density in-situ monitoring system and monitoring method
CN118225712A (en) * 2024-05-24 2024-06-21 江苏省环境监测中心 Lake surface blue algae spectrum image acquisition device

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117092100A (en) * 2023-08-22 2023-11-21 华南理工大学 Image processing-based intermittent sampling detection method and related device for water environment
CN117092100B (en) * 2023-08-22 2024-03-22 华南理工大学 Image processing-based intermittent sampling detection method and related device for water environment
CN117890258A (en) * 2024-03-15 2024-04-16 水利部交通运输部国家能源局南京水利科学研究院 Non-contact visual algae density in-situ monitoring system and monitoring method
CN117890258B (en) * 2024-03-15 2024-05-14 水利部交通运输部国家能源局南京水利科学研究院 Non-contact visual algae density in-situ monitoring system and monitoring method
CN118225712A (en) * 2024-05-24 2024-06-21 江苏省环境监测中心 Lake surface blue algae spectrum image acquisition device

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