Cladocerans-based cyanobacterial bloom early warning method and system

Abstract

The invention discloses a cyanobacterial bloom early warning method and system based on cladocerans. The method provided by the invention comprises the following steps: S101, based on basic geographic information of a target water area, collecting a remote sensing image of the target water area; s102, judging the distribution range of cyanobacterial blooms in the target water area according to the remote sensing image of the target water area; s103, if the cyanobacterial bloom exists in the target water area, removing a water area corresponding to the cyanobacterial bloom in the target water area to obtain a water area to be sampled, dividing the water area to be sampled into at least one first sampling point, and collecting the cladocera density of the at least one first sampling point; and S104, performing early warning on cyanobacterial bloom according to the relationship between the cladocera density and a preset threshold value. By combining a remote sensing technology and a sampling technology, the water body can be accurately monitored, so that a basis is provided for treatment and early warning of cyanobacterial bloom.

Description

Technical Field

[0001] The invention relates to the technical field of environmental engineering, and in particular to a cladoceran-based cyanobacteria bloom early warning method and system. Background Art

[0002] Eutrophication of water bodies refers to the phenomenon that under the influence of human activities, nutrients such as nitrogen and phosphorus required by organisms enter slow-flowing water bodies such as lakes, rivers, bays, etc. in large quantities, causing algae and other zooplankton to reproduce rapidly, the amount of dissolved oxygen in the water body to decrease, the water quality to deteriorate, and fish and other organisms to die in large numbers. When eutrophication occurs in water bodies, algae reproduce in large numbers and form algal blooms. Therefore, it is of great significance to timely grasp the community composition and density of algae in water bodies and monitor and warn of blue algae blooms for the protection of water ecological environment.

[0003] At present, the research on cyanobacteria bloom warning mainly includes: (1) using remote sensing technology to warn of cyanobacteria bloom, but there are still some shortcomings such as poor accuracy, low detection frequency, and only monitoring after the cyanobacteria bloom, and failure to warn of cyanobacteria bloom. (2) Classic indicator factor critical value warning, that is, selecting appropriate influencing factors as indicator factors of cyanobacteria bloom. However, the occurrence of cyanobacteria bloom is the result of the combined effects of physics, chemistry and biology, and it will only occur when many influencing factors meet a certain coupling relationship. Therefore, using a single influencing factor for cyanobacteria bloom warning cannot actually fully reflect the real environment of the water body, and there may be certain deviations in the warning. Summary of the invention

[0004] In order to overcome the above problems, the inventors propose a cladoceran-based cyanobacteria bloom early warning method and system.

[0005] In a first aspect, the present invention provides a cladoceran-based cyanobacterial bloom early warning method, comprising:

[0006] S101. Based on the basic geographic information of the target water area, collect remote sensing images of the target water area;

[0007] S102, judging the distribution range of the cyanobacteria bloom in the target water area according to the remote sensing image of the target water area;

[0008] S103, if there is a cyanobacterial bloom in the target water area, remove the water area corresponding to the cyanobacterial bloom in the target water area to obtain a water area to be sampled, divide the water area to be sampled into at least one first sampling point, and collect the cladoceran density of the at least one first sampling point;

[0009] S104. Based on the relationship between the density of cladocerans and a preset threshold, an early warning is issued for cyanobacteria blooms.

[0010] In a second aspect, the present invention provides a cladoceran-based cyanobacteria bloom early warning system, comprising:

[0011] A remote sensing acquisition module is used to acquire remote sensing images of the target waters based on the basic geographic information of the target waters;

[0012] A range acquisition module is used to determine the distribution range of cyanobacteria blooms in target waters based on remote sensing images of target waters;

[0013] A cladoceran collection module is used to remove the water area corresponding to the cyanobacterial bloom in the target water area if there is a cyanobacterial bloom in the target water area, obtain the water area to be sampled, divide the water area to be sampled into at least one first sampling point, and collect the cladoceran density of the at least one first sampling point;

[0014] The early warning module is used to issue an early warning for cyanobacteria blooms based on the relationship between the density of cladocerans and a preset threshold.

[0015] The cladoceran-based cyanobacteria bloom early warning method and system provided by the present invention have the following beneficial effects:

[0016] (1) The method provided by the present invention can accurately monitor water bodies by combining remote sensing technology and sampling technology, thereby providing a basis for the control and early warning of cyanobacterial blooms;

[0017] (2) The method provided by the present invention can provide a quantitative scientific basis for the prevention and control of cyanobacterial blooms, thereby realizing real-time monitoring of water bodies. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram showing the process of the cladoceran-based cyanobacteria bloom early warning method of the present invention;

[0019] Figure 2 The schematic diagram of the structure of the cladoceran-based cyanobacteria bloom early warning system of the present invention is shown;

[0020] Figure 3 This is a schematic diagram of the grade of the Baiyangdian water body. DETAILED DESCRIPTION

[0021] The present invention will be further described in detail below through the accompanying drawings and embodiments. Through these descriptions, the characteristics and advantages of the present invention will become more clear and distinct.

[0022] The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise noted.

[0023] The monitoring and early warning of cyanobacterial blooms using only remote sensing technology or sampling technology may result in errors.

[0024] Therefore, the present invention provides a cyanobacteria bloom early warning method and system based on cladocerans. In this method, the distribution range of cyanobacteria blooms can be identified by remote sensing technology, and the density of cladocerans can be used to warn of cyanobacteria blooms, thereby improving the real-time and accuracy of cyanobacteria bloom early warning. The present invention can provide a quantitative scientific basis for the prevention and control of cyanobacteria blooms, thereby realizing real-time monitoring of water bodies.

[0025] In a first aspect, the present invention provides a cyanobacteria bloom early warning method based on cladocerans. Figure 1 As shown, the method mainly includes:

[0026] S101. Based on the basic geographic information of the target water area, remote sensing images of the target water area are collected.

[0027] In the present invention, the size of the target water area is selected according to the actual situation of the water body, for example, the entire lake, a part of a lake, a section of a river flowing through a city, etc. can be selected.

[0028] In the present invention, satellite remote sensing images with infrared (630nm-690nm) and near infrared (760nm-900nm) bands are selected for use.

[0029] S102. Obtain the distribution range of the cyanobacteria bloom in the target water area based on the remote sensing image of the target water area.

[0030] In the present invention, step S102 may further include:

[0031] S102-1. Preprocess the remote sensing image of the target water area to obtain a preprocessed remote sensing image, wherein the preprocessed remote sensing image includes a plurality of water body pixels.

[0032] In the present invention, the remote sensing image includes land, water and other parts. By preprocessing the remote sensing image, the water image element in the remote sensing image, that is, the pixel unit of the water body, can be obtained.

[0033] In the present invention, preprocessing includes geometric correction, spatial clipping, radiation calibration, apparent reflectance calculation, atmospheric correction, and water body extraction, etc. It can be performed using existing methods without special limitation.

[0034] S102-2, obtaining a normalized vegetation index of each water body pixel, and comparing the normalized vegetation index of each water body pixel with a preset normalized vegetation index threshold.

[0035] Specifically, the Normalized Difference Vegetation Index (NDVI) refers to the ratio of the near-infrared band reflectance to the infrared band reflectance in remote sensing images, which can reflect the bloom of cyanobacteria, as shown below:

[0036]

[0037] Among them, NDVI is the normalized difference vegetation index; R nir is the near-infrared reflectivity, R red is the reflectivity in the infrared band.

[0038] Among them, when cyanobacteria blooms occur, green algae gather on the surface of the water body, and under the influence of external forces (such as wind, water flow, etc.), they accumulate over a large area and produce spectral characteristics similar to those of terrestrial plants on remote sensing images. The reflectance spectrum of the water body containing cyanobacteria blooms has obvious absorption peaks in the blue band and infrared band, and has a "reflection peak platform effect" similar to the characteristics of the plant spectrum curve in the near-infrared band, and the reflectivity of the near-infrared band is higher. Normal water bodies have a strong absorption effect on the near-infrared band, resulting in a lower reflectivity. Therefore, by utilizing the characteristic that the NDVI of cyanobacteria blooms in water bodies is higher than that of normal water bodies, cyanobacteria blooms can be distinguished from normal water bodies and cyanobacteria blooms can be extracted.

[0039] For NDVI, the preset normalized vegetation index threshold is used to effectively distinguish cyanobacterial blooms from normal water bodies, thereby extracting cyanobacterial blooms. The discriminant can be expressed as:

[0040] NDVI>NDVI t

[0041] Among them, NDVI t is the preset Normalized Difference Vegetation Index threshold, preferably NDVI t = 0. For water body pixels, if the NDVI of the water body pixel is greater than the preset normalized difference vegetation index threshold, the water body pixel is identified as a cyanobacteria bloom pixel, otherwise it is a normal water body pixel.

[0042] S102-3. Determine the distribution range of the cyanobacteria bloom based on the comparison results.

[0043] Specifically, according to the comparison result of the normalized vegetation index of each water body pixel and the preset normalized vegetation index threshold, all cyanobacteria bloom pixels and normal water body pixels are obtained. The image block composed of all cyanobacteria bloom pixels is the distribution range of cyanobacteria bloom.

[0044] Preferably, the cyanobacteria bloom area can be calculated by multiplying the total number of cyanobacteria bloom pixels by the actual area corresponding to each cyanobacteria bloom pixel, wherein the actual area corresponding to each cyanobacteria bloom pixel is determined by the spatial resolution of the remote sensing image.

[0045] Since remote sensing technology can only identify the corresponding waters of cyanobacteria blooms, and the identification of other waters is poor, the present invention further monitors the water body by means of sampling technology, thereby providing a timely and effective basis for cyanobacteria bloom early warning.

[0046] S103. If there is a cyanobacterial bloom in the target water area, remove the water area corresponding to the cyanobacterial bloom in the target water area to obtain a water area to be sampled, divide the water area to be sampled into at least one first sampling point, and collect the cladoceran density of the at least one first sampling point.

[0047] In the present invention, the number of first sampling points is determined according to the ecosystem in which the water area to be sampled is located, the distance between the first sampling point and the boundary of the water area corresponding to the cyanobacteria bloom (i.e., the boundary between the cyanobacteria bloom and the normal water body), etc. For example, the closer the distance to the boundary of the water area corresponding to the cyanobacteria bloom, the more the number of first sampling points can be.

[0048] Preferably, within the corresponding water area to be sampled which is greater than or equal to 3m and less than or equal to 10m away from the boundary of the cyanobacteria bloom, an average of 100 m 2 Set a first sampling point;

[0049] In the corresponding sampling waters that are greater than 10m and less than or equal to 20m away from the boundary of the cyanobacteria bloom, an average of 15 to 20m 2 Set a first sampling point;

[0050] In the corresponding sampling waters that are more than 20m away from the boundary of the cyanobacteria bloom, the average sampling rate is every 30-40m. 2 Set a first sampling point.

[0051] Preferably, step S103 may further include:

[0052] If there is no cyanobacterial bloom in the target water area, the target water area is divided into at least one second sampling point, and the cladoceran density of the at least one second sampling point is collected.

[0053] In the present invention, the number of the second sampling points is determined according to the size, boundary and ecosystem of the target water area.

[0054] Preferably, within the corresponding water area that is greater than 0m and less than or equal to 10m from the boundary of the target water area, an average of every 10 to 15m 2 Set a second sampling point;

[0055] In the corresponding waters that are greater than 10m and less than or equal to 30m away from the boundary of the target waters, an average of every 15 to 50m 2 Set a second sampling point;

[0056] In the corresponding waters that are more than 30m away from the boundary of the target waters, an average of 40 to 70m 2 Set a second sampling point.

[0057] In the present invention, it is preferred to sample each first sampling point or second sampling point by an unmanned ship. A plurality of water samplers are arranged on the unmanned ship, and the number of the water samplers corresponds to the number of the first sampling points or the second sampling points. The unmanned ship completes the sampling of each first sampling point or the second sampling point in turn.

[0058] In the present invention, if there is a cyanobacteria bloom in the target waters, the range of the cyanobacteria bloom is generally dispersed and the area is large. If there is no cyanobacteria bloom in the target waters, there will usually be aquatic plants such as floating plants and submerged plants, or artificial nets, etc. Therefore, it is necessary to plan the expected route of the unmanned boat so that the unmanned boat can bypass the cyanobacteria bloom, or aquatic plants or artificial nets, etc. in time and accurately reach each first sampling point or second sampling point.

[0059] In the present invention, the expected route of the unmanned ship is planned so that the unmanned ship reaches the first sampling point or the second sampling point according to the expected route.

[0060] Specifically, the actual angular position, actual angular velocity and actual angular acceleration of the unmanned ship are obtained in real time. Preferably, a sensor is provided on the unmanned ship, and the sensor can measure the actual angular position, actual angular velocity and actual angular acceleration of the unmanned ship in real time. And

[0061] The route error is obtained according to the expected angular position and the actual angular position, and the route error is quickly converged through the sliding mode control method to ensure that the unmanned ship can reach the first sampling point or the second sampling point along the expected route.

[0062] More specifically, in the sliding mode control method, the error dynamics model can be expressed as follows:

[0063] e1=qq d

[0064]

[0065] Where e1 represents the angular position error vector; q represents the actual angular position of the unmanned ship, that is, the angle between the unmanned ship and the horizontal line; q d represents the desired angular position; M0(q) represents the nominal value of the positive definite mass inertia matrix; u represents the control law of the unmanned ship; represents the sum of unknown disturbance and noise; Represents the nominal value of the sum of the Coriolis force and gravity.

[0066] More specifically, in the sliding mode control method, the sliding surface is a fast terminal sliding surface, and the sliding surface s can be expressed by the following formula:

[0067] s=e2+αe1+βe1 γ sgn(e1)

[0068] Wherein, s represents the sliding surface; α, β, and γ are design parameters; preferably, α>0, β>0, 1<γ<2.

[0069] In the present invention, a fast terminal sliding surface is adopted to make the unmanned boat more sensitive.

[0070] More specifically, in the sliding mode control method, the reaching law It can be expressed by the following formula:

[0071]

[0072] in, represents the reaching law; k1, a, k2 are design parameters, preferably, k1, k2>0, a>0; sigmoid is the function symbol;

[0073] Preferably, sigmoid(s) is set to Wherein, b is a design parameter, preferably, b>0.

[0074] Research has found that the use of the above-mentioned reaching law can achieve a smooth and fast control effect.

[0075] More specifically, in the sliding mode control method, the control law can be expressed as follows:

[0076]

[0077] In the present invention, the control law of the above formula is used to enable the unmanned ship to navigate along the desired route.

[0078] Usually, zooplankton includes protozoa, rotifers, crustaceans, etc. Crustaceans are usually divided into two major groups: copepods and cladocerans. Cladocera, also known as "daphnia", are commonly known as red worms and belong to the order Cladocera of the subclass Branchiopoda of the class Crustacea.

[0079] The study found that cladocerans have the most significant control effect on the density of cyanobacteria. Therefore, the density of cladocerans can effectively warn the risk level of cyanobacteria blooms.

[0080] In the present invention, the method for obtaining the density of cladocerans includes:

[0081] (1-1) Pour 10L of water from the water sampler into a No. 25 zooplankton net for filtration and concentration, place it into a 20mL glass bottle, and add 4% formalin solution for fixation;

[0082] (1-2) Let the fixed sample stand for 24 hours and then adjust the volume to 30 mL;

[0083] (1-3) Zooplankton was identified by visual observation under a microscope, and all zooplankton groups were identified to species. Protozoa were identified and counted using a 0.1 mL counting frame, and whole-slide counts were performed under a microscope at 100-400 times. Rotifers were identified and counted using a 1 mL counting frame, and whole-slide counts were performed. Copepods and cladocerans were identified and counted using a 5 mL counting frame, and whole-slide counts were performed under a low-power microscope.

[0084] (1-4) The results of the 5 mL whole slide count of cladocerans were converted into the number per liter of water (ind·L -1 , i.e. density).

[0085] S104. Based on the relationship between the density of cladocerans and a preset threshold, an early warning is issued for cyanobacteria blooms.

[0086] In the invention, when the density of cladocerans is less than or equal to a preset threshold, the first sampling point or the second sampling point is at an extremely high risk level (the probability of cyanobacterial bloom is greater than 75%);

[0087] When the density of cladocerans is greater than a preset threshold and less than or equal to 1.5 times the preset threshold, the first sampling point or the second sampling point is at a high risk level (the probability of cyanobacteria bloom is greater than 50%);

[0088] When the density of cladocerans is greater than 1.5 times the preset threshold and less than or equal to 2 times the preset threshold, the first sampling point or the second sampling point is at a medium risk level (the probability of cyanobacterial bloom is 25% to 50%);

[0089] When the density of cladocerans is greater than 2 times the preset threshold and less than or equal to 3 times the preset threshold, the first sampling point or the second sampling point is at a low risk level (the probability of cyanobacterial bloom is 5% to 25%);

[0090] When the density of cladocerans is greater than 3 times the preset threshold, the first sampling point or the second sampling point is at a risk-free level (the probability of cyanobacteria bloom is less than 5%).

[0091] Different combinations or degrees of many influencing factors in the ecosystem may affect the preset threshold. At the same time, the preset threshold obtained in different regions may also be different. However, after a large number of studies, it was found that when the preset threshold is 7 to 9 ind·L -1 , preferably 7.40 to 8.55 ind·L -1 When the risk level of blue algae blooms is accurately determined,

[0092] In the present invention, the relationship between the first sampling point or the second sampling point and the cyanobacterial bloom can be determined based on the cladoceran density at the first sampling point or the second sampling point, so that an early warning can be given for the corresponding water area, thereby providing more space for the control and early warning of cyanobacterial blooms.

[0093] For example, a water pixel is classified as a normal water pixel, but the water area is tested for cladocerans, and the corresponding cladoceran density is 8.70 ind·L -1 , it means that the water area is at a high risk level, so an early warning can be issued for the water area.

[0094] In the present invention, by combining remote sensing technology with sampling technology, the water body can be accurately monitored, thereby providing a basis for the control and early warning of blue algae blooms.

[0095] In a second aspect, the present invention provides a cyanobacteria bloom early warning system based on cladocerans. Figure 2 As shown, the system mainly includes:

[0096] A remote sensing acquisition module 201 is used to acquire remote sensing images of the target water area based on the basic geographic information of the target water area;

[0097] A range acquisition module 202 is used to determine the distribution range of cyanobacteria bloom in the target water area according to the remote sensing image of the target water area;

[0098] The cladoceran collection module 203 is used to remove the water area corresponding to the cyanobacterial bloom in the target water area if there is a cyanobacterial bloom in the target water area, obtain the water area to be sampled, divide the water area to be sampled into at least one first sampling point, and collect the cladoceran density of the at least one first sampling point;

[0099] The early warning module 204 is used to issue an early warning for cyanobacteria blooms based on the relationship between the density of cladocerans and a preset threshold.

[0100] The cladoceran-based cyanobacterial bloom early warning system provided by the present invention can be used to execute the cladoceran-based cyanobacterial bloom early warning method described in the first aspect above. Its implementation principle and technical effects are similar and will not be repeated here.

[0101] The present invention is further described below by means of specific examples, but these examples are merely exemplary and do not constitute any limitation to the protection scope of the present invention.

[0102] Example

[0103] Example 1

[0104] Early warning of blue algae blooms was issued for the water bodies of Baiyangdian. Baiyangdian is located at 38°43′~39°02′N, 115°38′~116°07′E. Remote sensing images of Baiyangdian were collected and preprocessed to obtain preprocessed remote sensing images, wherein the preprocessed remote sensing images include multiple water body pixels.

[0105] The preprocessing includes geometric correction, spatial clipping, radiation calibration, apparent reflectance calculation, atmospheric correction, and water body extraction, which is completed using ENVI remote sensing image processing software.

[0106] Get the Normalized Difference Vegetation Index (NDVI) of each water pixel. When the NDVI of a water pixel is greater than the preset Normalized Difference Vegetation Index threshold (NDVI t =0), the water body pixel is identified as a cyanobacteria bloom pixel, otherwise it is a normal water body pixel.

[0107] Based on the comparison results, the distribution range of cyanobacteria blooms can be obtained.

[0108] The water area corresponding to the cyanobacteria bloom is eliminated from the target water area to obtain the water area to be sampled, at least one first sampling point is set in the water area to be sampled, and the density of cladocerans at at least one first sampling point is collected.

[0109] In the corresponding sampling waters that are greater than or equal to 3m and less than or equal to 10m away from the boundary of the cyanobacteria bloom, an average of every 10m 2 Set a first sampling point;

[0110] In the corresponding sampling waters that are greater than 10m and less than or equal to 20m away from the boundary of the cyanobacteria bloom, an average of 20m 2 Set a first sampling point;

[0111] In the corresponding sampling waters that are more than 20m away from the boundary of the cyanobacteria bloom, an average of 40m 2 Set a first sampling point.

[0112] Each first sampling point is sampled by an unmanned boat. A plurality of water samplers are arranged on the unmanned boat, and the number of the water samplers corresponds to the number of the first sampling points. The unmanned boat completes the sampling of each first sampling point in turn.

[0113] Plan the desired route of the unmanned ship so that the unmanned ship arrives at the first sampling point along the desired route.

[0114] Obtain the actual angular position, actual angular velocity and actual angular acceleration of the unmanned ship in real time;

[0115] The route error is obtained according to the expected angular position and the actual angular position, and the route error is quickly converged through the sliding mode control method to ensure that the unmanned ship can reach the first sampling point along the expected route.

[0116] In the sliding mode control method, the error dynamics model is expressed by the following formula:

[0117] e1=qq d

[0118]

[0119] Where e1 represents the angular position error vector; q represents the actual angular position of the unmanned ship, that is, the angle between the unmanned ship and the horizontal line; q d represents the desired angular position; M0(q) represents the nominal value of the positive definite mass inertia matrix; u represents the control law of the unmanned ship; represents the sum of unknown disturbance and noise; Represents the nominal value of the sum of the Coriolis force and gravity.

[0120] In the sliding mode control method, the sliding surface is a fast terminal sliding surface, and the sliding surface s is expressed by the following formula:

[0121] s=e2+αe1+β|e1| γ sgn(e1)

[0122] Where s represents the sliding surface; the values ​​of α, β, and γ are 0.02, 0.2, and 1.5, respectively.

[0123] In the sliding mode control method, the reaching law It is expressed by the following formula:

[0124]

[0125] in, represents the convergence law; the values ​​of k1, a, and k2 are 0.2, 4, and 0.02 respectively; sigmoid is the function symbol;

[0126] Among them, the value of b is 0.1.

[0127] In the sliding mode control method, the control law is expressed as follows:

[0128]

[0129] The density of cladocerans is obtained as follows:

[0130] (2-1) Pour 10L of water from the water sampler into a No. 25 zooplankton net for filtration and concentration, place in a 20mL glass bottle, and add 4% formalin solution for fixation;

[0131] (2-2) After the fixed sample is allowed to stand for 24 hours, the volume is adjusted to 30 mL;

[0132] (2-3) Zooplankton was identified by visual observation under a microscope, and all zooplankton groups were identified to species. Protozoa were identified and counted using a 0.1 mL counting frame, and whole-slide counting was performed under a microscope at 100-400 times. Rotifers were identified and counted using a 1 mL counting frame, and whole-slide counting was performed. Copepods and cladocerans were identified and counted using a 5 mL counting frame, and whole-slide counting was performed under a low-power microscope.

[0133] (2-4) The results of the 5 mL full slide count of cladocerans were converted into the number per liter of water (ind·L -1 , i.e. density).

[0134] Based on the relationship between the density of cladocerans and the preset threshold, an early warning of cyanobacteria blooms is issued, where the preset threshold is 8.55 ind·L -1 .

[0135] When the density of cladocerans is less than or equal to the preset threshold, the first sampling point is at an extremely high risk level;

[0136] When the density of cladocerans is greater than a preset threshold and less than or equal to 1.5 times the preset threshold, the first sampling point is at a high risk level;

[0137] When the density of cladocerans is greater than 1.5 times the preset threshold and less than or equal to 2 times the preset threshold, the first sampling point is at a medium risk level;

[0138] When the density of cladocerans is greater than 2 times the preset threshold and less than or equal to 3 times the preset threshold, the first sampling point is at a low risk level;

[0139] When the density of cladocerans is greater than 3 times the preset threshold, the first sampling point is at a risk-free level.

[0140] The 2019 test results are available at Figure 3 .from Figure 3 It can be seen that in 2019, the extremely high-risk and high-risk waters of Baiyangdian Lake were basically located in the southern part of the lake area. Among them, extremely high-risk waters mean severely eutrophic waters. High-risk waters also mean that they are in a eutrophic state, but the degree of pollution is less than or equal to that of extremely high-risk waters. This is consistent with the spatial distribution of eutrophication of Baiyangdian Lake water bodies pointed out in the literature (Liu Xin, Shi Bin, Meng Jing, et al. Temporal and spatial variation characteristics of eutrophication and sediment pollution in Baiyangdian Lake [J]. Environmental Science, 2020, 41(05): 2127-2136).

[0141] The present invention has been described above in conjunction with preferred embodiments, but these embodiments are only exemplary and serve only as an illustration. On this basis, the present invention may be subjected to a variety of substitutions and improvements, all of which fall within the scope of protection of the present invention.

Claims

1. A cladoceran-based cyanobacterial bloom early warning method, characterized in that: include: S101. Based on the basic geographic information of the target water area, collect remote sensing images of the target water area; S102, determining the distribution range of the cyanobacteria bloom in the target water area according to the remote sensing image of the target water area; S103, if there is a cyanobacterial bloom in the target water area, remove the water area corresponding to the cyanobacterial bloom from the target water area to obtain a water area to be sampled, divide the water area to be sampled into at least one first sampling point, and collect the cladoceran density of the at least one first sampling point; S104. Based on the relationship between the cladoceran density and a preset threshold, an early warning is issued for cyanobacteria bloom.

2. The cladoceran-based cyanobacteria bloom early warning method according to claim 1, characterized in that: Step S102 includes: S102-1, preprocessing the remote sensing image of the target water area to obtain a preprocessed remote sensing image, wherein the preprocessed remote sensing image includes a plurality of water body pixels; S102-2, obtaining a normalized vegetation index of each water body pixel, and comparing the normalized vegetation index of each water body pixel with a preset normalized vegetation index threshold; S102-3. Determine the distribution range of the cyanobacteria bloom based on the comparison results.

3. The cladoceran-based cyanobacteria bloom early warning method according to claim 2, characterized in that: In step S102-2, If the normalized difference vegetation index of the water body pixel is greater than a preset normalized difference vegetation index threshold, the water body pixel is a cyanobacteria bloom pixel, otherwise the water body pixel is a normal water body pixel.

4. The cladoceran-based cyanobacteria bloom early warning method according to claim 1, characterized in that: In step S103, In the corresponding sampling waters that are greater than or equal to 3m and less than or equal to 10m away from the boundary of the cyanobacteria bloom, an average of 5 to 10m 2 Set a first sampling point; In the corresponding sampling waters that are greater than 10m and less than or equal to 20m away from the boundary of the cyanobacteria bloom, an average of 15 to 20m 2 Set a first sampling point; In the corresponding sampling waters that are more than 20m away from the boundary of the cyanobacteria bloom, the average sampling rate is every 30-40m. 2 Set a first sampling point.

5. The cladoceran-based cyanobacteria bloom early warning method according to claim 1, characterized in that: Step S103 also includes: If there is no cyanobacterial bloom in the target water area, the target water area is divided into at least one second sampling point, and the cladoceran density of the at least one second sampling point is collected.

6. The cladoceran-based cyanobacteria bloom early warning method according to claim 5, characterized in that: Plan the desired route of the unmanned ship so that the unmanned ship reaches the first sampling point or the second sampling point according to the desired route.

7. The cladoceran-based cyanobacteria bloom early warning method according to claim 6, characterized in that: include: Obtain the actual angular position, actual angular velocity and actual angular acceleration of the unmanned ship in real time; The route error is obtained according to the expected angular position and the actual angular position, and the route error is quickly converged through the sliding mode control method to ensure that the unmanned ship can reach the first sampling point or the second sampling point along the expected route.

8. The cladoceran-based cyanobacteria bloom early warning method according to claim 1, characterized in that: In step S104, When the density of cladocerans is less than or equal to a preset threshold, the first sampling point is at an extremely high risk level; When the density of cladocerans is greater than a preset threshold and less than or equal to 1.5 times the preset threshold, the first sampling point is at a high risk level; When the density of cladocerans is greater than 1.5 times the preset threshold and less than or equal to 2 times the preset threshold, the first sampling point is at a medium risk level; When the density of cladocerans is greater than 2 times the preset threshold and less than or equal to 3 times the preset threshold, the first sampling point is at a low risk level; When the density of cladocerans is greater than 3 times the preset threshold, the first sampling point is at a risk-free level.

9. A cladoceran-based cyanobacterial bloom early warning system, characterized in that: include: A remote sensing acquisition module is used to acquire remote sensing images of the target waters based on the basic geographic information of the target waters; A range acquisition module, which is used to determine the distribution range of cyanobacteria bloom in the target water area according to the remote sensing image of the target water area; A cladoceran collection module, which is used to, if there is a cyanobacterial bloom in the target water area, remove the water area corresponding to the cyanobacterial bloom in the target water area to obtain a water area to be sampled, divide the water area to be sampled into at least one first sampling point, and collect the cladoceran density of the at least one first sampling point; An early warning module is used to warn the cyanobacteria bloom based on the relationship between the density of the cladocerans and a preset threshold.

10. The cladoceran-based cyanobacteria bloom early warning system according to claim 9, characterized in that: In the early warning module, When the density of cladocerans is less than or equal to a preset threshold, the first sampling point is at an extremely high risk level; When the density of cladocerans is greater than a preset threshold and less than or equal to 1.5 times the preset threshold, the first sampling point is at a high risk level; When the density of cladocerans is greater than 1.5 times the preset threshold and less than or equal to 2 times the preset threshold, the first sampling point is at a medium risk level; When the density of cladocerans is greater than 2 times the preset threshold and less than or equal to 3 times the preset threshold, the first sampling point is at a low risk level; When the density of cladocerans is greater than 3 times the preset threshold, the first sampling point is at a risk-free level.

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