A system and method for dynamic monitoring and precise management of blue-green algae in rivers and lakes

Abstract

The application discloses a kind of river and lake blue-green algae dynamic monitoring and accurate management system and method, the monitoring system includes satellite 1, unmanned aerial vehicle 2, monitoring station 3, river and lake 4;Monitoring station 3 is set on the bank of river and lake 4, unmanned aerial vehicle 2 is linked with satellite 1 by network.Control unmanned aerial vehicle by satellite, accurate positioning monitoring, real-time measurement blue-green algae content in river and lake water, the data obtained is finally sent to database by satellite to achieve real-time monitoring, execute command, feedback data;And the polluted area is managed by unmanned aerial vehicle, has the advantages, such as dynamic real-time monitoring, data timely and accurate, operation management is simple, cost is low and measurement is extensive, etc., provides prevention and control measures for the outbreak monitoring, early warning and accurate management of river and lake blue-green algae.

Description

Technical Field

[0001] The present invention belongs to the field of environmental protection and data monitoring, and specifically relates to a system and method for dynamic monitoring and precise control of blue-green algae in rivers and lakes. Background Art

[0002] Cyanobacteria gain a competitive advantage in eutrophic waters by efficiently absorbing and utilizing nutrients like nitrogen and phosphorus. Their unique intracellular nutrient absorption and transport systems allow them to rapidly accumulate nitrogen and phosphorus in high-nutrient environments, storing ample "energy" for their vigorous growth. As their numbers surge, they proliferate explosively, eventually forming algal blooms. Once these blooms occur, they cause a chain reaction of damage to aquatic ecosystems. Dissolved oxygen is depleted, suffocating aquatic animals and hindering photosynthesis in aquatic plants. This disrupts the fundamental links of the food chain, threatening the entire ecological cycle. Furthermore, cyanobacteria release algal toxins during their growth, metabolism, and decomposition. These toxins can enter drinking water systems through water sources or through the food chain, posing a serious threat to drinking water safety. Comprehensive measures are urgently needed to address the crisis. While my country has paid considerable attention to cyanobacteria outbreaks, there is a lack of timely, scientific, and effective means for real-time monitoring and early warning of cyanobacteria in rivers and lakes. Consequently, the need for effective monitoring and control of cyanobacteria in rivers and lakes is becoming increasingly urgent.

[0003] Chinese patent application number CN202410650766.8, titled "Lake Surface Cyanobacteria Spectral Image Acquisition Device," discloses a lake surface cyanobacteria spectral image acquisition device, relating to the field of cyanobacteria monitoring. The device comprises a collection probe and a container, the container comprising a first cavity and a second cavity. An airbag is disposed within the first cavity, a carrier having a locking member disposed on one side for securing the container, a movable member connected to one side of the carrier, and a first blocking member disposed on the movable member to shift the locking member from a fixed state for securing the container to a released state for releasing the container. The lake surface cyanobacteria spectral image acquisition device, by providing a container capable of automatically completing sampling, can obtain the required water samples in a timely manner, avoiding the problem of data deviation caused by the lag caused by the inability to obtain water samples in a timely manner. This device provides accurate data support for the established estimation model, and when the collection probe detects abnormal data, it can promptly collect water samples from the monitored area for analysis of the cause of the abnormality, thereby improving the accuracy of spectral image display and prediction during real-time monitoring. Although this patent provides a cyanobacteria monitoring method, its monitoring range is relatively small.

[0004] Chinese patent application number CN202310445529.3, "A System and Device for Monitoring Blue-green Algae in Water," discloses a system and device for monitoring blue-green algae in water. The system and device fall within the field of water detection technology and specifically include: an atlas library storing images of various stages of blue-green algae growth; a data acquisition module for real-time acquisition of water images, generating a collection of water images, collecting water samples in real time, and detecting water sample data; an image processing module for constructing a blue-green algae growth recognition model based on the atlas library, inputting the water images into the model, and identifying the growth stages of blue-green algae in the collection of water images; and a data optimization module for extracting water images that the blue-green algae recognition model cannot identify, obtaining corresponding water samples, collecting close-up images of the water samples, and determining the presence of blue-green algae in the water based on the close-up images. The present invention automatically monitors blue-green algae in real time based on images of the water surface to be tested, and issues early warnings based on the intensity of the blue-green algae outbreak. While this patent provides a method for detecting blue-green algae, the method is relatively cumbersome and cannot achieve real-time monitoring.

[0005] Chinese patent application number CN202223299341.2, "A Blue-green Algae Monitoring Buoy," discloses a blue-green algae monitoring buoy comprising a buoy carrier, a monitoring host mounted on the buoy carrier, a monitoring tube fixed at the bottom center of the buoy carrier, a multi-parameter water quality monitoring sensor mounted inside the monitoring tube, and connected to the monitoring host via a data cable. An annular rotating member is provided on the outside of the buoy carrier. This utility model enables automatic, remote, and real-time collection and monitoring of water quality information, eliminating the need for manual sampling and monitoring in shifts, saving manpower. The monitoring tube provides built-in protection for the multi-parameter water quality monitoring sensor. Furthermore, when the water carries floating debris toward the buoy carrier, multiple blades located on the outside of the annular frame automatically disperse the floating debris, preventing it from accumulating on the buoy carrier and affecting the accuracy of water quality monitoring. While this patent provides a device for blue-green algae monitoring, it lacks the ability to perform intelligent analysis and processing to provide feedback. Summary of the Invention

[0006] In response to the current status of control measures for cyanobacteria content in rivers and lakes, the present invention provides a system and method for dynamic monitoring and precise control of cyanobacteria in rivers and lakes. Based on the monitoring and calculation of the growth range and growth rate of cyanobacteria in rivers and lakes, the system controls the cyanobacteria in a timely, accurate and scientific manner, thereby further optimizing the long-term and stable development of rivers and lakes.

[0007] Technical solution: The purpose of the present invention is achieved through the following technical solutions:

[0008] A system for the dynamic monitoring and precise management of cyanobacteria in rivers and lakes primarily comprises a satellite 1, an unmanned aerial vehicle (UAV) 2, a monitoring station 3, and a river or lake 4. Monitoring station 3 is located on the shore of river or lake 4, and UAV 2 is connected to satellite 1 via a network. Satellite 1 comprises a solar panel 1-1, a signal receiver 1-2, and an image collector 1-3; UAV 2 comprises a drug dispenser 2-1, a power system 2-2, a signal receiver 2-3, and an image collector 2-4; monitoring station 3 comprises a signal transceiver 3-1, a data storage center 3-2, and a data processor 3-3; and river or lake 4 contains a cyanobacteria colony 4-1, aquatic plants 4-2, and aquatic animals 4-3.

[0009] The monitoring system of satellite 1 conducts long-term monitoring of rivers and lakes. When the amount of blue-green algae in rivers and lakes reaches a set threshold, the satellite will send a command to the data terminal; after receiving the command from the data system, the group of drones 2 will fly to the blue-green algae pollution area to further take on-site photos and transmit the photos to the monitoring station (3); the data processor (3-3) of monitoring station 3 processes the photos taken by the drones to provide monitoring data; based on the received monitoring data, the satellite determines whether algaecides and algal toxin inhibitors need to be released. After the group of drones 2 carries the drugs, they operate in real time according to the received instructions, realizing dynamic monitoring and precise control of blue-green algae in rivers and lakes.

[0010] The system operation mode:

[0011] Step 1: Use satellite 1 to monitor the cyanobacteria area in the river or lake. Image collectors 1-3 obtain the cyanobacteria area of ​​each river or lake in real time. There are n cyanobacteria monitored in M ​​rivers and lakes, which are defined as M1, M2, M3, ..., M n ; The area of ​​each cyanobacteria region is S1, S2, S3, S4, ..., S n , where n is the number of cyanobacteria areas in rivers and lakes; and the data is sent to the data storage center 3-2 of the monitoring station 3. Based on formula (1), the data processor 3-3 of the monitoring station 3 calculates the cyanobacteria ratio P in rivers and lakes;

[0012]

[0013] If P < 50%, it is determined that there is no excessive growth trend of blue algae in rivers and lakes, and the drug dosing device does not need to add algaecides. At the same time, the monitoring station continues to monitor the rivers and lakes in real time;

[0014] If P ≥ 50%, it is determined that there is an excessive growth trend of cyanobacteria in rivers and lakes; proceed to step 2;

[0015] Step 2: UAV 2 receives the signal from satellite 1 for further monitoring, collects images through image collectors 2-4, and determines the area of ​​cyanobacteria S in the river and lake at time t,m1 1,t,m1 , S 2,t,m1 , S 3,t,m1 , S4,t,m1 ,…,S n,t,m1 The concentration of cyanobacteria at each point is A 1,t,m1 , A 2,t,m1 , A 3,t,m1 , A 4,t,m1 ,…,A n,t,m1 ; Where n is the number of drones; and the data is sent to the data storage center 3-2 of the monitoring station 3;

[0016] Based on formula (2), for A i，t，m1 >A0, the data processor 3-3 of the monitoring station 3 calculates the cumulative amount of blue algae exceeding the standard rate at each point W i , (i=1~n)

[0017] W i =∫(A i，t，m1 -A0)dS i Formula (2)

[0018] Where A0 represents the lower limit of cyanobacteria concentration, which is 5-20 million / L.

[0019] At the same time, the drug dispenser 2-1 of each UAV calculates the dosage of algaecide X based on formula (3): i , and go to step 3.

[0020] X i =k1×W i Formula (3)

[0021] Wherein, k1 represents the conversion coefficient of algaecide dosage, which is 0.01-0.5 μg / piece.

[0022] Step 3: After the algaecide reacts fully with the cyanobacteria, at time t,m1', the image collectors 2-4 of each drone 2 monitor the cyanobacteria concentration at each point again, which is A 1,t,m1’ , A 2,t,m1’ , A 3,t,m1’ , A 4,t,m1’ ,…,A n,t,m1’ Where n is the number of drones, and the time interval between t,m1' and t,m1 is the time required for the algaecide to fully react with the cyanobacteria. The data is then sent to the data storage center 3-2 of the monitoring station 3. The data processor 3-3 of the monitoring station 3 then determines whether the limit value is met after each drone has added the algaecide:

[0023] If A 1,t,m1’ , A 2,t,m1’ , A 3,t,m1’ , A 4,t,m1’ ,…,A n,t,m1’ All meet A i，t，m1′<A0,(i=1~n),则判定投加除藻剂后不存在蓝藻数量超限的情况,进入步骤4;反之则在t,m2时刻返回步骤2,其中t,m2与t,m1之间的时间间隔为无人机监测最低时间间隔。

[0024] Step 4: The image collectors 2-4 of each UAV monitor the area S of cyanobacteria after the reaction 1,t,m’ , S 2,t,m’ , S 3,t,m’ , S 4,t,m’ ,…,S n,t,m’ The concentration of algal toxins at each point is B1, B2, B3, B4, ..., B n Where n is the number of drones; and the data is sent to the data storage center 3-2 of the monitoring station 3. Then the data processor 3-3 of the monitoring station 3 determines whether there is an algal toxin excess after the reaction is completed:

[0025] If B1, B2, B3, B4, ..., B n All meet B i <B0,(i=1~n),则判定不存在藻毒素超限的情况,药物投加装置无需投加藻毒素抑制剂。

[0026] Otherwise, the data storage center 3-2 of the monitoring station 3 calculates the algal toxin accumulation exceeding the limit V according to formula (4):

[0027]

[0028] Then, the drug dosing module of each UAV calculates the dosage Y of the algae toxin inhibitor based on formula (5), and returns to step 1.

[0029] Y=k2×V Formula (5)

[0030] Where, k2 represents the conversion coefficient of the dosage of algae toxin inhibitor, and its value is as follows:

[0031] For ozone, take 1-5 mg / L;

[0032] For potassium permanganate, take 1-5 mg / L;

[0033] For powdered activated carbon (PAC), use 1-15 mg / L.

[0034] In step 1, a grid method is used to monitor rivers and lakes using satellites. The specific steps are as follows:

[0035] Set grid lines in the length a and width b directions of rivers and lakes according to the following rules 1 to 3;

[0036] Rule 1: If a or b is less than 30m, draw a grid line at the midpoint;

[0037] Rule two: if a or b is within the range of 30-60m, then divide one grid line at 1 / 3 and 2 / 3 positions;

[0038] Rule three: if a or b is greater than 60m, then divide one grid line every 20m from the midpoint;

[0039] In step one, if P n ≥ 50%, it is determined that cyanobacteria overgrowth in the lake, and the monitoring station sends instructions to the drug-carrying UAV to carry out management in the lake, and the drug-carrying UAV does not change the amount of drug delivery, and needs to continuously monitor the cyanobacteria in the lake during the management.

[0040] In step two, when 50%≤P<65%, the conversion coefficient k1 of the algaecide dosage is taken as a lower value (0.01-0.2 μg / individual); when 65%≤P n <85%, the conversion coefficient k1 of the algaecide dosage is taken as an intermediate value (0.2-0.4 μg / individual); and when P n > 85%, the conversion coefficient k1 of the algaecide dosage is taken as a higher value (0.4-0.5 μg / individual).

[0041] In step three, at time t, m2, compared with time t, m1, the moving direction of each UAV is determined according to the following rules:

[0042] The position of each UAV at time t, m1 is set as wherein, represents the distance of the i-th UAV from the origin in the long direction, represents the distance of the i-th UAV from the origin in the short direction. And the initial speed of each UAV is given as wherein, represents the moving speed of the i-th UAV in the long direction, represents the moving speed of the i-th UAV in the short direction.

[0043] At time t, m2, the speed of each UAV is updated according to formula (6);

[0044]

[0045] And the speed of each UAV is updated according to formula (7);

[0046]

[0047] In the formula, A a+1，t，m1 is the cyanobacteria concentration monitored by the UAV i closest to the UAV on the far side of the origin in the long direction a; and A a-1，t，m1A is the cyanobacteria concentration obtained by monitoring the drone that is closest to the origin and adjacent to drone i in the direction of the long side a; b+1，t，m1 A is the cyanobacteria concentration obtained by monitoring the UAV that is close to UAV i and farther from the origin in the direction of the short side b; b-1，t，m1 is the cyanobacteria concentration measured by the drone closest to drone i, closer to the origin, in the direction of short side b; k3 is the acceleration constant. After updating, this data is fed back to the power system 2-2 of each drone 2.

[0048] Compared with the prior art, the advantages of the present invention are:

[0049] (1) This invention closely follows the key needs of river and lake ecological management, focuses on real-time monitoring of core elements, accurately benchmarks established material measurement indicators, and uses an intelligent and comprehensive tracking and monitoring system to enable rivers and lakes to develop sustainably and healthily.

[0050] (2) The monitoring system has excellent performance, high-efficiency monitoring capabilities, and can operate stably and efficiently for a long time.

[0051] (3) The monitoring system is easy and convenient to operate, and the overall cost is controllable, with the characteristics of low investment and low energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a diagram showing the operating principle of the dynamic monitoring and precise management system of the present invention;

[0053] Figure 2 is a top view of UAV 2;

[0054] Figure 3 is a front view of UAV 2;

[0055] Figure 4 Schematic diagram of the connection relationship between the dynamic monitoring and precise management system of the present invention;

[0056] In the picture: Satellite-1, UAV-2, Monitoring Station-3, River and Lake-4;

[0057] Solar panel-1-1, signal receiver-1-2, image collector-1-3; drug dispenser-2-1, power system-2-2, signal receiver-2-3 and image collector-2-4; signal transceiver-3-1, data storage center-3-2, data processor-3-3; cyanobacteria colony-4-1, aquatic plants-4-2, aquatic animals-4-3. DETAILED DESCRIPTION

[0058] The technical solutions of the present invention are further described through the following specific embodiments.

[0059] Reference Figure 1 as well as Figure 3As shown, a system for dynamic monitoring and precise control of blue-green algae in rivers and lakes includes a satellite 1, a drone 2, a monitoring station 3, and rivers and lakes 4.

[0060] The satellite 1 includes a solar panel 1-1, a signal receiver 1-2 and an image collector 1-3; the signal receiver 1-2 is used to receive data transmitted by the monitoring station 3, and the solar panel provides kinetic energy; the drone 2 includes a drug dispenser 2-1, a power system 2-2, a signal receiver 2-3 and an image collector 2-4; the image collector 2-4 is used to take pictures of the cyanobacteria contaminated area, the signal receiver 2-3 receives instructions from the satellite, and the drug dispenser 2-1 carries the required drugs and administers them to the cyanobacteria contaminated area; the monitoring station 3 includes a signal transceiver 3-1, a data storage center 3-2 and a data processor 3-3, the signal transceiver 3-1 receives pictures taken by the image collector 2-4 of the drone 2 and stores them in the data storage center 3-2, the data processor 3-3 processes the stored pictures, outputs monitoring data and transmits them to the satellite; the river and lake 4 includes a cyanobacteria group 4-1, aquatic plants 4-2 and aquatic animals 4-3; the monitoring station 3 is set on the bank of the river and lake 4, and the drone 2 is connected to the satellite 1 via a network.

[0061] Example 1

[0062] A river or lake is designed with a slope of 1:3, a length of 300m, a width of 100m, and a water depth of 2m. Various aquatic plants and animals are cultivated in the lake or river.

[0063] The grid method is used to monitor the interior of rivers and lakes using satellites. Since a and b are greater than 60m, a grid line is drawn every 20m starting from the midpoint of the river or lake.

[0064] After the river and lake system stabilizes, satellite imagery will be collected and analyzed daily between 8:00 AM and 9:00 AM. The primary water quality assessment indicator is the Surface Water Environmental Quality Standard (GB 3838-2002).

[0065] The specific operation steps are as follows: Step 1: Satellite 1 monitors the blue algae area in the river and lake with the UAV, and the image collectors 1-3 obtain the blue algae area in the river and lake in real time, and obtain the river and lake monitoring area M1-1, M2-1, M3-1, ..., M n -1; cyanobacteria area S1-1, S2-1, S3-1, S4-1, ..., S n -1, where n is the number of cyanobacteria areas in the river and lake; and the data is sent to the data storage center 3-2 of the monitoring station 3. Based on formula (1), the data processor 3-3 of the monitoring station 3 calculates the cyanobacteria ratio of the river and lake P1;

[0066]

[0067] The calculation shows that P1 = 43% < 50%, which determines that there is no excessive growth trend of blue-green algae in rivers and lakes. The drug dosing device does not need to add algaecides. At the same time, the monitoring station continues to monitor the rivers and lakes in real time.

[0068] Example 2

[0069] A river or lake is designed with a slope of 1:3, a length of a = 100 m, a width of b = 50 m, and a water depth of 2 m. Various aquatic plants and animals are cultivated in the lake or river.

[0070] The grid method is used to monitor rivers and lakes using satellites. Since a>60m, a grid line is divided every 20m from the midpoint of the long side. Since b is within the range of 30-60m, a grid line is divided at 1 / 3 and 2 / 3 of the width.

[0071] After the river and lake system stabilizes, satellite imagery will be collected and analyzed daily between 8:00 AM and 9:00 AM. The primary water quality assessment indicator is the Surface Water Environmental Quality Standard (GB 3838-2002).

[0072] The specific steps are as follows:

[0073] Step 1: Satellite 1 pairs of drones monitor the blue algae area in rivers and lakes, and image collectors 1-3 obtain the blue algae area in rivers and lakes in real time, and obtain the river and lake monitoring area M1-2, M2-2, M3-2, ..., M n -2; cyanobacteria area S1-2, S2-2, S3-2, S4-2, ..., S n -2, where n is the number of cyanobacteria areas in rivers and lakes; and the data is sent to the data storage center 3-2 of the monitoring station 3. Based on formula (1), the data processor 3-3 of the monitoring station 3 calculates the cyanobacteria ratio of rivers and lakes P2;

[0074]

[0075] The calculated value of P2 = 68% > 50% indicates that the blue algae in rivers and lakes have an excessive growth trend; proceed to step 2.

[0076] Step 2: UAV 2 receives the signal from satellite 1 for further monitoring, collects images through image collectors 2-4, and determines the area of ​​cyanobacteria S in the river and lake at time t,m1 1,t,m1 -2, S 2,t,m1 -2, S 3,t,m1 -2, S 4,t,m1 -2,…,S n,t,m1 -2; the concentration of cyanobacteria at each point is A 1,t,m1 -2, A 2,t,m1 -2, A 3,t,m1 -2, A 4,t,m1 -2,…,An,t,m1 -2; where n is the number of drones; and sends the data to the data storage center 3-2 of the monitoring station 3

[0077] Based on formula (2), for A i，t，m1 >A0, the data processor 3-3 of the monitoring station 3 calculates the cumulative amount of blue algae exceeding the standard rate at each point W i , (i=1~n)

[0078] W i =∫(A i，t，m1 -A0)dS i Formula (2)

[0079] Where A0 represents the lower limit of cyanobacteria concentration, which is taken as 10 million / L.

[0080] At the same time, the drug dispenser 2-1 of each UAV calculates the dosage of algaecide X based on formula (3): i , and go to step 3.

[0081] X i =k1×W i Formula (3)

[0082] Wherein, k1 represents the conversion coefficient of algaecide dosage. And since 65%≤P2=68%<85%, k1 takes the middle value, k1 takes the middle value of 0.25μg / piece.

[0083] Step 3: After the algaecide reacts fully with the cyanobacteria, at time t,m1', the image collectors 2-4 of each drone 2 monitor the cyanobacteria concentration at each point again, which is A 1,t,m1’ -2, A 2,t,m1’ -2, A 3,t,m1’ -2, A 4,t,m1’ -2,…,A n,t,m1’ -2; where n is the number of drones, and the time interval between t,m1' and t,m1 is the time required for the algaecide to fully react with the cyanobacteria; and the data is sent to the data storage center 3-2 of the monitoring station 3.

[0084] Then the data processor 3-3 of the monitoring station 3 determines whether the limit value is met after the algaecide is added to each drone:

[0085] Because A 1,t,m1’ -2, A 2,t,m1’ -2, A 3,t,m1’ -2, A 4,t,m1’ -2,…,A n,t,m1’ -2 all meet A i，t，m1′ <A0,(i=1~n),则判定投加除藻剂后不存在蓝藻数量超限的情况,进入步骤四;

[0086] Step 4: The image collectors 2-4 of each UAV monitor the area S of cyanobacteria after the reaction 1,t,m’ -2, S 2,t,m’ -2, S 3,t,m’ -2, S 4,t,m’ -2,…,S n,t,m’ -2; the concentration of algae toxins at each point is B1-2, B2-2, B3-2, B4-2, ..., B n -2. Where n is the number of drones; and the data is sent to the data storage center 3-2 of the monitoring station 3. Then, the data processor 3-3 of the monitoring station 3 determines whether there is an excessive amount of algal toxins after the reaction is completed:

[0087] Since B1-2, B2-2, B3-2, B4-2, ..., B n -2 all meet B i <B0,(i=1~n),判定不存在藻毒素超限的情况,药物投加装置无需投加藻毒素抑制剂,返回步骤一。

[0088] Example 3

[0089] A river or lake is designed with a slope of 1:3, a length of 50m, a width of 25m, and a water depth of 2m. Various aquatic plants and animals are cultivated in the lake or river.

[0090] The grid method is used to monitor rivers and lakes using satellites. Since a is within the range of 30-60m, one grid line is divided at the 1 / 3 and 2 / 3 positions on the long side; and since b is less than 30m, one grid line is divided at the midpoint of the short side.

[0091] After the river and lake system stabilizes, satellite imagery will be collected and analyzed daily between 8:00 AM and 9:00 AM. The primary water quality assessment indicator is the Surface Water Environmental Quality Standard (GB 3838-2002).

[0092] The specific steps are as follows:

[0093] Step 1: Satellite 1 pairs of drones monitor the blue algae area in rivers and lakes, and image collectors 1-3 obtain the blue algae area in rivers and lakes in real time, and obtain the river and lake monitoring area M1-3, M2-3, M3-3, ..., M n -3; cyanobacteria area S1-3, S2-3, S3-3, S4-3, ..., S n -3, where n is the number of cyanobacteria areas in rivers and lakes; and the data is sent to the data storage center 3-2 of the monitoring station 3. Based on formula (1), the data processor 3-3 of the monitoring station 3 calculates the cyanobacteria ratio of rivers and lakes P2;

[0094]

[0095] The calculated value of P2 = 88% > 50% indicates that the blue algae in rivers and lakes have an excessive growth trend; proceed to step 2.

[0096] Step 2: UAV 2 receives the signal from satellite 1 for further monitoring, collects images through image collectors 2-4, and determines the area of ​​cyanobacteria S in the river and lake at time t,m1 1,t,m1 -3, S 2,t,m1 -3, S 3,t,m1 -3, S 4,t,m1 -3,…,S n,t,m1 -3; the concentration of cyanobacteria at each point is A 1,t,m1 -3, A 2,t,m1 -3, A 3,t,m1 -3, A 4,t,m1 -3,…,A n,t,m1 ; Where n is the number of drones; and the data is sent to the data storage center 3-2 of the monitoring station 3.

[0097] Based on formula (2), for A i，t，m1 >A0, the data processor 3-3 of the monitoring station 3 calculates the cumulative amount of blue algae exceeding the standard rate at each point W i , (i=1~n)

[0098] W i =∫(A i，t，m1 -A0)dS i Formula (2)

[0099] Where A0 represents the lower limit of cyanobacteria concentration.

[0100] At the same time, the drug dispenser 2-1 of each UAV calculates the dosage of algaecide X based on formula (3): i , and go to step 3.

[0101] X i =k1×W i Formula (3)

[0102] Where k1 represents the conversion coefficient of algaecide dosage. Since Pn>85%, the coefficient k1 takes a higher value of 0.5μg / piece.

[0103] Step 3: After the algaecide reacts fully with the cyanobacteria, at time t,m1', the image collectors 2-4 of each drone 2 monitor the cyanobacteria concentration at each point again, which is A 1,t,m1’ -3, A 2,t,m1’ -3, A 3,t,m1’ -3, A 4,t,m1’ -3,…,A n,t,m1’-3; where n is the number of drones, and the time interval between t,m1' and t,m1 is the time required for the algaecide to fully react with the cyanobacteria; and the data is sent to the data storage center 3-2 of the monitoring station 3. The data processor 3-3 of the monitoring station 3 then determines whether the limit value is met after the algaecide is added to each drone:

[0104] Because A 2,t,m1’ -3>A0, (i=1~n), it is determined that the number of cyanobacteria still exceeds the limit after adding the algaecide, and it is necessary to return to step 2 at time t,m2;

[0105] At time t,m2, compared to time t,m1, the movement direction of each drone is determined according to the following rules:

[0106] Set the position of each drone at time t,m1 to in, represents the distance of the i-th drone from the origin in the long side direction, Indicates the distance of the i-th drone from the origin in the short direction. And gives each drone an initial speed in, Indicates the moving speed of the i-th drone in the long side direction, Indicates the moving speed of the i-th UAV in the short side direction.

[0107] At time t,m2, the speed of each UAV is updated according to formula (6);

[0108]

[0109] And update the speed of each UAV according to formula (7);

[0110]

[0111] Where A a+1，t，m1 A is the cyanobacteria concentration obtained by monitoring the UAV that is close to UAV i and farther from the origin in the direction of the long side a; a-1，t，m1 A is the cyanobacteria concentration obtained by monitoring the drone that is closest to the origin and adjacent to drone i in the direction of the long side a; b+1，t，m1 A is the cyanobacteria concentration obtained by monitoring the UAV that is close to UAV i and farther from the origin in the direction of the short side b; b-1，t，m1 is the cyanobacteria concentration obtained by monitoring the UAV close to the origin in the direction of the short side b; k3 is the acceleration constant.

[0112] After updating, the data is fed back to the power system (2-2) of each UAV (2).

[0113] Step 2: UAV 2 receives the signal from satellite 1 for further monitoring, collects images through image collectors 2-4, and determines the area of ​​cyanobacteria S in the river and lake at time t,m2 1,t,m2 -3, S 2,t,m2 -3, S 3,t,m2 -3, S 4,t,m2 -3,…,S n,t,m2 -3; the concentration of cyanobacteria at each point is A 1,t,m2 -3, A 2,t,m2 -3, A 3,t,m2 -3, A 4,t,m2 -3,…,A n,t,m2 -3; where n is the number of drones; and sends the data to the data storage center 3-2 of the monitoring station 3.

[0114] Based on formula (2), for A i，t，m2 >A0, the data processor 3-3 of the monitoring station 3 calculates the cumulative amount of blue algae exceeding the standard rate at each point W i , (i=1~n)

[0115] W i =∫(A i，t，m1 -A0)dS i Formula (2)

[0116] Where A0 represents the lower limit of cyanobacteria concentration, which is 10 million / L.

[0117] At the same time, the drug dispenser 2-1 of each UAV calculates the dosage of algaecide X based on formula (3): i , and go to step 3.

[0118] X i =k1×W i Formula (3)

[0119] Where k1 represents the conversion coefficient of algaecide dosage. Since Pn>85%, the coefficient k1 takes a higher value of 0.5μg / piece.

[0120] Step 3: After the algaecide reacts fully with the cyanobacteria, at time t,m2', the image collectors 2-4 of each drone 2 monitor the cyanobacteria concentration at each point again, which is A 1,t,m2’ -3, A 2,t,m2’ -3, A 3,t,m2’ -3, A 4,t,m2’ -3,…,A n,t,m2’ -3; where n is the number of drones, and the time interval between t,m1' and t,m1 is the time required for the algaecide to fully react with the cyanobacteria; and the data is sent to the data storage center 3-2 of the monitoring station 3.

[0121] Then the data processor 3-3 of the monitoring station 3 determines whether the limit value is met after the algaecide is added to each drone:

[0122] Because A 1,t,m2’ -3, A 2,t,m2’ -3, A 3,t,m2’ -3, A 4,t,m2’ -3,…,A n,t,m2’ -3 all meet A i，t，m2′ <A0,(i=1~n),则判定投加除藻剂后不存在蓝藻数量超限的情况,进入步骤四;

[0123] Step 4: The image collectors 2-4 of each UAV monitor the area S of cyanobacteria after the reaction 1,t,m’ -3, S 2,t,m’ -3, S 3,t,m’ -3, S 4,t,m’ -3,…,S n,t,m’ -3; the concentration of algal toxins at each point is B1-3, B2-3, B3-3, B4-3, ..., B n -3. Where n is the number of drones; and the data is sent to the data storage center 3-2 of the monitoring station 3. Then, the data processor 3-3 of the monitoring station 3 determines whether there is an excessive amount of algal toxins after the reaction is completed:

[0124] Since B4-2>B0, (i=1-n), it is determined that the algae toxin exceeds the limit. The drug dosing device needs to add algae toxin inhibitors. The data storage center 3-2 of the monitoring station 3 calculates the algae toxin cumulative limit V according to formula (4):

[0125]

[0126] Then, the drug dosing module of each UAV calculates the dosage Y of the algae toxin inhibitor based on formula (5), and returns to step 1.

[0127] Y=k2×V Formula (5)

[0128] Wherein, k2 represents the conversion coefficient of the dosage of the algae toxin inhibitor. In this embodiment, ozone is used as the algae toxin inhibitor, and k2 is taken as 2.5 mg / L.

[0129] The foregoing is merely an embodiment of the present invention and is not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.

Claims

1. A method for dynamic monitoring and precise control of blue algae in rivers and lakes, characterized by: The following steps are involved: Step 1: Use satellite (1) to control the UAV to monitor the cyanobacteria area in the river and lake in real time. The image collector (2-4) of the UAV obtains the cyanobacteria area of ​​each river and lake in real time. Where n cyanobacteria are monitored in M ​​rivers and lakes, which are defined as M1, M2, M3, ..., M n ; Cyanobacteria area S1, S2, S3, S4, ..., S n , where n is the number of cyanobacteria areas in rivers and lakes; and the data is sent to the data storage center (3-2) of the monitoring station (3). Based on formula (1), the data processor (3-3) of the monitoring station (3) calculates the ratio P of the cyanobacteria area to the river and lake area, and transmits the data to the satellite; If P < 50%, it is determined that there is no excessive growth trend of blue algae in rivers and lakes, and the drug dosing device does not need to add algaecides. At the same time, the monitoring station continues to monitor the rivers and lakes in real time; If P ≥ 50%, it is determined that the blue algae in the river or lake have an excessive growth trend; go to step 2; Step 2: The drone (2) receives the signal from the satellite (1) for further monitoring, collects images through the image collector (2-4), and determines the area S of cyanobacteria in the river and lake at time t,m1 1,t,m1 , S 2,t,m1 , S 3,t,m1 , S 4,t,m1 ,…,S n,t,m1 The concentration of cyanobacteria at each point is A 1,t,m1 , A 2,t,m1 , A 3,t,m1 , A 4,t,m1 ,…,A n,t,m1 ; Where n is the number of drones; and sends the data to the data storage center (3-2) of the monitoring station (3); Based on formula (2), for A i，t，m1 For the points where the monitoring station (3) is larger than A0, the data processor (3-3) of the monitoring station (3) calculates the cumulative amount of blue algae exceeding the standard rate W at each point. i , i=1~n; W i =∫(A i,t,m1 -A0)dS i Formula (2) Where A0 represents the lower limit of cyanobacteria concentration, which is 20 million / L; At the same time, based on formula (3), the drug dispenser (2-1) of each drone calculates the dosage of algaecide X i , and implement the drug administration, then go to step 3: X i =k1×W i Formula (3) Wherein, k1 represents the conversion coefficient of algaecide dosage, which is 0.01-0.5 μg / piece; Step 3: The algaecide to be added fully reacts with the cyanobacteria. At time t, m1', the image collectors (2-4) of each drone (2) monitor the cyanobacteria concentrations at each point again, which are A 1,t,m1’ , A 2,t,m1’ , A 3,t,m1’ , A 4,t,m1’ ,…,A n,t,m1’ Where n is the number of drones, and the time interval between t,m1' and t,m1 is the time required for the algaecide to fully react with the cyanobacteria. The data is sent to the data storage center (3-2) of the monitoring station (3). The data processor (3-3) of the monitoring station (3) then determines whether the limit value is met after the algaecide is added to each drone: If A 1,t,m1’ , A 2,t,m1’ , A 3,t,m1’ , A 4,t,m1’ , …, A n,t,m1’ all satisfy A i，t，m1′ < A0, (i = 1~n), it is determined that there is no situation where the number of cyanobacteria exceeds the limit after adding the algaecide, and proceed to step 4; otherwise, return to step 2 at times t, m2, where the time interval between t, m2 and t, m1 is the minimum time interval for UAV monitoring; Step 4: The image collectors (2-4) of each UAV monitor the area S of cyanobacteria after the reaction 1,t,m’ , S 2,t,m’ , S 3,t,m’ , S 4,t,m’ ,…,S n,t,m’ The concentration of algal toxins at each point is B1, B2, B3, B4, ..., B n ; Where n is the number of drones; and the data is sent to the data storage center (3-2) of the monitoring station (3); and then the data processor (3-3) of the monitoring station (3) determines whether there is an algal toxin exceeding the limit after the reaction is completed: If B1, B2, B3, B4, …, B n all satisfy B i < B0, (i = 1 to n), it is determined that there is no situation where the algal toxin exceeds the limit, and the algal toxin inhibitor does not need to be added by the drug dosing device; Otherwise, the data storage center (3-2) of the monitoring station (3) calculates the algal toxin accumulation limit V according to formula (4): Then, based on formula (5), the drug dosing module of each drone calculates the dosage Y of the algae toxin inhibitor and returns to step 1, ultimately achieving dynamic monitoring and precise control of blue-green algae in rivers and lakes; Y=k2×V Formula (5) Wherein, k2 represents the conversion coefficient of the dosage of algae toxin inhibitor. For ozone, it is 1-5 mg / L; for potassium permanganate, it is 1-5 mg / L; for powdered activated carbon (PAC), it is 1-15 mg / L.

2. The method for dynamic monitoring and precise control of blue algae in rivers and lakes according to claim 1, characterized in that: In step 1, the grid method is used to monitor rivers and lakes using satellites, and grid lines are set in the length a and width b directions of the rivers and lakes according to the following rules 1 to 3; Rule 1: If a or b is less than 30m, draw a grid line at the midpoint of the length or width; Rule 2: If a or b is within the range of 30-60m, draw a grid line at the 1 / 3 and 2 / 3 positions along the length respectively; Rule 3: If a or b is greater than 60m, a grid line is drawn every 20m starting from the midpoint of the length.

3. The method for dynamic monitoring and precise control of blue algae in rivers and lakes according to claim 1, characterized in that: In step 1, if P ≥ 50%, it is determined that the cyanobacteria in the rivers and lakes are overgrown. The monitoring station will send instructions to the drone carrying medicine to treat the cyanobacteria in the rivers and lakes. The amount of medicine released by the drone will remain unchanged. The cyanobacteria in the rivers and lakes need to be continuously monitored during the treatment period.

4. The method for dynamic monitoring and precise control of blue algae in rivers and lakes according to claim 1, characterized in that: In step 2, when 50%≤P<65%, the algaecide dosage conversion coefficient k1 takes a lower value k1=0.01-0.2μg / piece; when 65%≤P n <85%, the conversion coefficient k1 of algaecide dosage takes the middle value k1=0.2-0.4μg / piece; P n When the algaecide dosage is greater than 85%, the conversion coefficient k1 takes a higher value, k1 = 0.4-0.5 μg / piece.

5. The method for dynamic monitoring and precise control of blue algae in rivers and lakes according to claim 1, characterized in that: In step 3, at time t,m2, compared to time t,m1, the movement direction of each drone is determined according to the following rules: Set the position of each drone at time t,m1 to: in, represents the distance of the i-th UAV from the origin in the long side direction; represents the distance of the i-th UAV from the origin in the short side direction; And give each drone an initial speed: in, Indicates the moving speed of the i-th UAV in the long side direction; represents the moving speed of the i-th UAV in the short side direction; At time t,m2, the speed of each UAV is updated according to formula (6); And update the speed of each UAV according to formula (7); Where A a+1，t，m1 is the cyanobacteria concentration obtained by monitoring the UAV that is closest to UAV i and farther from the origin in the direction of the long side a; A a-1，t，m1 is the cyanobacteria concentration obtained by monitoring the UAV that is closest to the origin and is adjacent to UAV i in the direction of the long side a; A b+1，t，m1 is the cyanobacteria concentration obtained by monitoring the UAV that is close to UAV i and farther from the origin in the direction of the short side b; A b-1，t，m1 is the cyanobacteria concentration obtained by monitoring the UAV that is closest to the origin and is adjacent to UAV i in the direction of the short side b; k3 is the acceleration constant; After updating, the data is fed back to the power system (2-2) of each UAV (2).

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