Dynamic monitoring and precise treatment system and method for blue-green algae in rivers and lakes

Abstract

The invention discloses a dynamic monitoring and precise treatment system and method for blue-green algae in rivers and lakes. The monitoring system comprises a satellite 1, an unmanned aerial vehicle 2, a monitoring station 3 and the rivers and lakes 4. The monitoring station 3 is arranged on the bank side of the river and lake 4, and the unmanned aerial vehicle 2 is connected with the satellite 1 through a network. The unmanned aerial vehicle is controlled through a satellite, accurate positioning and monitoring are achieved, the content of blue-green algae in river and lake water is measured in real time, and obtained data are finally sent to a database through the satellite to achieve real-time monitoring, command execution and data feedback. The system has the advantages of dynamic real-time monitoring, timely and accurate data, simplicity and convenience in operation and management, low cost, wide measurement range and the like, and provides prevention and control measures for outbreak monitoring, early warning and precise treatment of blue-green algae in rivers and lakes.

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 algae in rivers and lakes. Background Art

[0002] Blue algae gain a competitive advantage in eutrophic water bodies by virtue of their efficient absorption and utilization of nutrient elements such as nitrogen and phosphorus. The unique nutrient absorption and transport system in its cells can quickly enrich nitrogen and phosphorus in a high nutrient concentration environment, and reserve sufficient "energy" for its vigorous growth. With the surge in numbers, the population proliferates explosively, and then accumulates to form algae blooms. Once algae blooms appear, the damage to the aquatic ecosystem will explode in a chain reaction. The dissolved oxygen in the water body is consumed in large quantities, aquatic animals suffocate due to lack of oxygen, aquatic plants are blocked from photosynthesis, the basic links of the food chain are broken, and the entire ecological cycle is on the verge of collapse; moreover, blue algae release algae toxins during the process of growth metabolism and death decomposition, or take water from water sources, or enrich along the food chain, and quietly sneak into the drinking water system. Drinking water safety is also facing severe challenges, and all-round response measures are urgently needed to resolve the crisis. my country has also paid a lot of attention to the outbreak of blue algae, but there is a lack of timely, scientific and effective means for real-time monitoring and early warning of blue algae in rivers and lakes. Therefore, how to achieve the monitoring and control of blue algae in rivers and lakes is becoming more and more urgent.

[0003] The Chinese patent application "Lake Surface Blue Algae Spectral Image Collection Device" with application number CN202410650766.8 discloses a lake surface blue algae spectral image collection device, which relates to the field of blue algae monitoring, including a collection probe and a container, the container including a first cavity and a second cavity, an air bag is arranged in the first cavity, a locking member for fixing the container is arranged on one side of the bearing member, a moving member is connected to one side of the bearing member, and a first blocking member is arranged on the moving member to change the locking member from a fixed state of fixing the container to a released state of releasing the container; the lake surface blue algae spectral image collection device, by setting a container that can automatically complete sampling, can obtain the required water body samples in time, avoid the problem of data deviation caused by the hysteresis caused by the failure to obtain water body samples in time, provide accurate data support for the established estimation model, and when the collection probe detects abnormal data, can collect water body samples in the monitoring area in time to analyze the cause of the abnormality, thereby improving the spectral image display and prediction accuracy during real-time monitoring. Although the patent provides a blue algae monitoring method, the monitoring range is small.

[0004] The Chinese patent application "A water body cyanobacteria monitoring system and device" with application number CN202310445529.3 discloses a water body cyanobacteria monitoring system and device, which belongs to the field of water body detection technology, and specifically includes: a map library, which stores images of various stages of cyanobacteria growth; a data acquisition module, which is used to collect water body images in real time, generate a water body image set, collect water body samples in real time, and detect water body sample data; an image processing module, which is used to build a cyanobacteria growth recognition model based on the map library, input the water body image into the cyanobacteria growth recognition model, and identify the growth stage of cyanobacteria in the water body image set; a data optimization module, which is used to extract water body images that cannot be recognized by the cyanobacteria recognition model, obtain corresponding water body samples, collect close-up images of water body samples, and determine whether there are cyanobacteria in the water body according to the close-up images; the present invention automatically monitors cyanobacteria in real time based on the image to be tested on the water surface, and issues an early warning based on the outbreak intensity results of cyanobacteria. Although the patent provides a method for detecting cyanobacteria, the method is relatively cumbersome and cannot achieve real-time monitoring.

[0005] The Chinese patent application "A Blue Algae Monitoring Buoy" with application number CN202223299341.2 discloses a blue algae monitoring buoy, including a buoy carrier, a monitoring host installed on the buoy carrier, a monitoring tube fixed at the bottom center of the buoy carrier, a multi-parameter water quality monitoring sensor installed on the inner side of the monitoring tube, and the multi-parameter water quality monitoring sensor is connected to the monitoring host through a data connection line; an annular rotating part is arranged on the outer side of the buoy carrier, and the utility model realizes automatic remote real-time collection and monitoring of water quality information, does not require manual shift sampling and monitoring, saves manpower, and sets a monitoring tube for built-in protection of the multi-parameter water quality monitoring sensor. At the same time, when the water flow carries floating garbage to the buoy carrier, the multiple blades located on the outer side of the annular sleeve automatically diffuse the floating garbage carried by the water flow, and prevent the garbage from gathering on the buoy carrier and affecting the accuracy of water quality monitoring. Although the patent provides a device for monitoring blue algae, it cannot provide feedback for intelligent analysis and processing. Summary of the invention

[0006] In view of 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. The system monitors and calculates the growth range and growth rate of cyanobacteria in rivers and lakes, and controls the cyanobacteria in a timely, accurate and scientific manner, so as to further and better optimize the long-term 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 dynamic monitoring and precise management of blue algae in rivers and lakes, mainly comprising a satellite 1, a drone 2, a monitoring station 3, and a river or lake 4; the monitoring station 3 is arranged on the bank of the river or lake 4, and the drone 2 is connected to the satellite 1 through a network. The satellite 1 comprises a solar panel 1-1, a signal receiver 1-2, and an image collector 1-3; the drone 2 comprises a drug dispenser 2-1, a power system 2-2, a signal receiver 2-3, and an image collector 2-4; the monitoring station 3 comprises a signal transceiver 3-1, a data storage center 3-2, and a data processor 3-3; and there are blue algae groups 4-1, aquatic plants 4-2, and aquatic animals 4-3 in the river or lake 4.

[0009] The monitoring system of satellite 1 conducts long-term monitoring of rivers and lakes. When the amount of blue 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 algae pollution area to further take photos of the scene and transmit the photos to the monitoring station (3); the data processor (3-3) of the monitoring station 3 processes the photos taken by the drone to give monitoring data; the satellite determines whether it is necessary to release algaecides and algae toxin inhibitors based on the received monitoring data, and the group of drones 2 will carry the drugs and operate in real time according to the received instructions, thereby realizing dynamic monitoring and precise control of blue algae in rivers and lakes.

[0010] The system operation mode:

[0011] Step 1: Use satellite 1 to monitor the blue algae area in the river or lake. Image collectors 1-3 obtain the blue algae area of ​​each river or lake in real time. M rivers and lakes monitor n blue algae, 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 blue algae 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 blue algae 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 the blue algae in the river or lake has an excessive growth trend; 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 S of blue algae 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 accumulated amount of blue algae exceeding the standard rate W at each point i , (i=1~n)

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

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

[0019] At the same time, based on formula (3), the drug dispenser 2-1 of each drone calculates the dosage of the algaecide X 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 and the blue algae have fully reacted, at time t,m1', the image collectors 2-4 of each drone 2 again monitor the blue algae concentrations at each point, 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 blue algae; 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 each drone meets the limit value after adding 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 to n), it is determined that there is no situation where the cyanobacteria quantity exceeds the limit after adding the algaecide, and proceed to step 4; otherwise, return to step 2 at time t,m2, where the time interval between t,m2 and t,m1 is the minimum time interval for UAV monitoring.

[0024] Step 4: The image collectors 2-4 of each UAV monitor the area S of the cyanobacteria after the reaction 1,t,m’ , S 2,t,m’ , S 3,t,m’ , S 4,t,m’ , …, S n,t,m’ ; The concentrations of microcystins at each point are B1, B2, B3, B4, …, B n . Among them, n is the number of UAVs; 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 a situation where the microcystin exceeds the limit after the reaction:

[0025] 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 microcystin exceeds the limit, and the drug dosing device does not need to add microcystin inhibitor.

[0026] Conversely, the data storage center 3-2 of the monitoring station 3 calculates the cumulative microcystin overlimit V according to formula (4):

[0027]

[0028] Then, based on formula (5), the drug dosing modules of each UAV calculate the dosing amount Y of the microcystin inhibitor and return to step one.

[0029] Y = k2 × V Formula (5)

[0030] In the formula, k2 represents the conversion coefficient of the microcystin inhibitor dosing amount, and the values are 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), take 1-15 mg / L.

[0034] Among them, in step one, the satellite is used to monitor the river and lake by the grid method, and the specific operation steps are as follows:

[0035] Set grid lines in the two directions of the length a and width b of the river and lake according to the following rules one to three;

[0036] Rule one: If a or b < 30 m, then divide 1 grid line at the midpoint;

[0037] 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 respectively;

[0038] Rule 3: If a or b>60m, start from the midpoint and divide a grid line every 20m;

[0039] Among them, in step 1, if P n If the level of blue algae in rivers and lakes is ≥50%, it is determined that the blue algae in rivers and lakes are overgrown. The monitoring station will send instructions to the drone carrying medicine to treat the rivers and lakes. The amount of medicine released by the drone carrying medicine remains unchanged. The blue algae in rivers and lakes need to be continuously monitored during the treatment period.

[0040] Among them, in step 2, when 50%≤P<65%, the algaecide dosage conversion coefficient k1 takes a lower value (0.01-0.2μg / piece); when 65%≤P n <85%, the conversion coefficient k1 of the algaecide dosage takes the middle value (0.2-0.4μg / piece); P n When the algaecide dosage is >85%, the conversion coefficient k1 takes a higher value (0.4-0.5 μg / piece).

[0041] Among them, in step 3, at time t,m2, compared with time t,m1, the movement direction of each drone is determined according to the following rules:

[0042] 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 direction, Indicates the distance of the i-th drone from the origin in the short direction. And gives each drone an initial speed in, represents 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.

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

[0044]

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

[0046]

[0047] In the formula, A a+1，t，m1 A is the concentration of cyanobacteria 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，m1A is the concentration of cyanobacteria obtained by monitoring the drone that is close to the origin and is adjacent to drone i in the direction of the long side a; b+1，t，m1 A is the concentration of cyanobacteria obtained by monitoring the UAV that is close to UAV i and far from the origin in the direction of the short side b; b-1，t，m1 is the concentration of blue algae monitored by the drones close to the origin and adjacent to drone 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 drone 2.

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

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

[0050] (2) The monitoring system has excellent performance, has 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, with controllable overall costs and low investment and energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a schematic diagram of the operation 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 the UAV 2;

[0055] Figure 4 A 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 group-4-1, aquatic plants-4-2, aquatic animals-4-3. DETAILED DESCRIPTION

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

[0059] Reference Figure 1 as well as Figure 3As shown, a system for dynamic monitoring and precise management of blue 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 pollution area, the signal receiver 2-3 receives the instructions of the satellite, the drug dispenser 2-1 carries the required drugs and administers the drugs to the cyanobacteria pollution 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 the photos 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 photos, outputs the 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 through a network.

[0061] Example 1

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

[0063] The grid method is used to monitor the river and lake with satellites. Since a and b>60m, a grid line is divided every 20m from the midpoint of the river and lake.

[0064] After the rivers and lakes are in stable operation, the satellite will collect and analyze images of the rivers and lakes once a day, with the sampling time fixed at 08:00 to 09:00. The main water quality assessment indicators are 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 drone, and the image collector 1-3 obtains the blue algae area in the river and lake in real time, and obtains the river and lake monitoring area M1-1, M2-1, M3-1, ..., M n -1; blue algae area S1-1, S2-1, S3-1, S4-1, ..., S n -1, where n is the number of blue algae areas in the river or 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 blue algae ratio of the river or lake P1;

[0066]

[0067] The calculation results show that P1=43%<50%, which means 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.

[0068] Example 2

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

[0070] The grid method is used to monitor rivers and lakes with 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 the 1 / 3 and 2 / 3 positions of the width.

[0071] After the rivers and lakes are in stable operation, the satellite will collect and analyze images of the rivers and lakes once a day, with the sampling time fixed at 08:00 to 09:00. The main water quality assessment indicators are the "Surface Water Environmental Quality Standard" (GB 3838-2002).

[0072] The specific steps are as follows:

[0073] Step 1: Satellite 1 monitors the blue algae area in the river and lake with the drone, and 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 M1-2, M2-2, M3-2, ..., M n -2; blue algae area S1-2, S2-2, S3-2, S4-2, ..., S n -2, where n is the number of blue algae areas in the river or 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 blue algae ratio of the river or lake P2;

[0074]

[0075] The calculated value of P2=68%>50% indicates that the blue algae in rivers and lakes have an excessive growth trend; execute 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 S of blue algae 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 the data is sent to the data storage center 3-2 of the monitoring station 3

[0077] Based on Equation (2), for point positions where A i，t，m1 > A0, the data processor 3-3 of the monitoring station 3 calculates the cumulative amount W of the cyanobacteria over-standard speed at each point position i , (i = 1~n)

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

[0079] In the formula, A0 represents the lower limit of the cyanobacteria concentration, which is taken as 10 million per liter.

[0080] At the same time, based on Equation (3), the chemical dosing device 2-1 of each drone calculates the dosing amount X of the algaecide i , and enters Step Three.

[0081] X i = k1 × W i Equation (3)

[0082] In the formula, k1 represents the conversion coefficient of the algaecide dosing amount. And since 65% ≤ P2 = 68% < 85%, k1 takes the middle value, and k1 takes the middle value of 0.25 μg / unit.

[0083] Step Three: After the algaecide and the cyanobacteria fully react, at time t, m1', the image collectors 2-4 of each drone 2 monitor the cyanobacteria concentrations at each point position again, which are 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 and the cyanobacteria to fully react; 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 dosing of the algaecide by each drone meets the limit value:

[0085] Since 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), it is determined that there is no situation where the number of cyanobacteria exceeds the limit after the algaecide is dosed, and it enters Step Four;

[0086] Step 4: The image collectors 2-4 of each drone monitor the area S of the 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 microcystin concentrations at each point are 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 a situation where the microcystin exceeds the limit after the reaction:

[0087] Since B1-2, B2-2, B3-2, B4-2, …, B n -2 all satisfy B i < B0, (i = 1~n), it is determined that there is no situation where the microcystin exceeds the limit, and the drug dosing device does not need to dose the microcystin inhibitor, and returns to Step 1.

[0088] Example 3

[0089] For a certain river and lake, the designed ground slope is 1:3, the length of the river and lake is a = 50m, the width of the river and lake is b = 25m, and the water depth is 2m. Various aquatic animals and plants are cultured in the river and lake.

[0090] The grid method is used to monitor the river and lake by satellite. Since a is in the range of 30-60m, one grid line is divided at the 1 / 3 and 2 / 3 positions of the long side; and since b < 30m, one grid line is divided at the midpoint of the short side.

[0091] After the river and lake operates stably, the satellite conducts image acquisition and analysis on the river and lake once a day, and the sampling time is fixed at 08:00~09:00. The main water quality assessment index is the Surface Water Environment Quality Standard (GB 3838-2002).

[0092] The specific operation steps are as follows:

[0093] Step 1: Satellite 1 monitors the cyanobacteria area in the river and lake by drones, and the image collector 1-3 obtains the cyanobacteria area in the river and lake in real time, and obtains the monitored areas M1-3, M2-3, M3-3, …, M n -3; The cyanobacteria areas S1-3, S2-3, S3-3, S4-3,..., S n -3, 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 Equation (1), the data processor 3-3 of the monitoring station 3 calculates the cyanobacteria occupancy rate P2 of the river and lake;

[0094]

[0095] The calculated value of P2=88%>50% indicates that the blue algae in rivers and lakes have an excessive growth trend; execute 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 S of blue algae 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 accumulated amount of blue algae exceeding the standard rate W at each point 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, based on formula (3), the drug dispenser 2-1 of each drone calculates the dosage of the algaecide X i , and proceed to step 3.

[0101] X i =k1×W i Formula (3)

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

[0103] Step 3: After the algaecide and the blue algae have fully reacted, at time t,m1', the image collectors 2-4 of each drone 2 again monitor the blue algae concentrations at each point, which are 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 blue algae; 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 each drone meets the limit value after adding the algaecide:

[0104] Because A 2,t,m1’ -3>A0, (i=1~n), it is determined that the number of blue algae 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 with 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 direction, Indicates the distance of the i-th drone from the origin in the short direction. And gives each drone an initial speed in, represents 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 equation (6);

[0108]

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

[0110]

[0111] In the formula, A a+1，t，m1 A is the concentration of cyanobacteria obtained by monitoring the UAV that is close to UAV i and far from the origin in the direction of the long side a; a-1，t，m1 A is the concentration of cyanobacteria obtained by monitoring the drone that is close to the origin and is adjacent to drone i in the direction of the long side a; b+1，t，m1 A is the concentration of cyanobacteria 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 drone (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 S of blue algae 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; wherein n is the number of drones; and the data is sent 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 accumulated amount of blue algae exceeding the standard rate W at each point i , (i=1~n)

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

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

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

[0118] X i =k1×W i Formula (3)

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

[0120] Step 3: After the algaecide and the blue algae have fully reacted, at time t,m2', the image collectors 2-4 of each drone 2 again monitor the blue algae concentrations at each point, which are 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; wherein 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 blue algae; and the data is sent to the data storage center 3-2 of the monitoring station 3.

[0121] Subsequently, the data processor 3-3 of the monitoring station 3 determines whether the limits are met after each drone adds the algaecide:

[0122] Since 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), it is determined that there is no situation where the cyanobacteria quantity exceeds the limit after adding the algaecide, and proceed to Step Four;

[0123] Step Four: The image collectors 2-4 of each drone monitor the area S of the 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 microcystin concentrations at each point are B1-3, B2-3, B3-3, B4-3,..., B n -3. Among them, n is the number of drones; and the data is sent to the data storage center 3-2 of the monitoring station 3. Subsequently, the data processor 3-3 of the monitoring station 3 determines whether there is a situation where the microcystin exceeds the limit after the reaction ends:

[0124] Since B4-2 > B0, (i = 1~n), it is determined that there is a situation where the microcystin exceeds the limit, and the drug dosing device needs to add the microcystin inhibitor. The data storage center 3-2 of the monitoring station 3 calculates the cumulative microcystin excess amount V according to Equation (4):

[0125]

[0126] Subsequently, based on Equation (5), the drug dosing modules of each drone calculate the dosing amount Y of the microcystin inhibitor and return to Step One.

[0127] Y = k2 × V Equation (5)

[0128] In the formula, k2 represents the conversion coefficient of the microcystin inhibitor dosing amount. In this embodiment, ozone is used as the microcystin inhibitor, and k2 is taken as 2.5 mg / L.

[0129] The above is only the embodiment of the present invention and is not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

Claims

1. A system for dynamic monitoring and precise management of blue algae in rivers and lakes, characterized by: It includes a satellite (1), a drone (2), a monitoring station (3), and a river or lake (4); the monitoring station (3) is set up on the bank of the river or lake (4), and the drone (2) is connected to the satellite (1) and the monitoring station (3) through a network; The satellite (1) includes a solar panel (1-1), a signal receiver (1-2) and an image collector (1-3); 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 monitoring station (3) includes a signal transceiver (3-1), a data storage center (3-2) and a data processor (3-3); the river and lake (4) include a blue algae colony (4-1), aquatic plants (4-2) and aquatic animals (4-3).

2. A method for dynamic monitoring and precise control of blue algae in rivers and lakes based on the system for dynamic monitoring and precise control of blue algae in rivers and lakes according to claim 1, characterized in that: The monitoring system of the satellite (1) conducts long-term monitoring of rivers and lakes. When the amount of blue 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 drone (2) group will fly to the blue algae pollution area to further take photos of the scene and transmit the photos to the monitoring station (3); the data processor (3-3) of the monitoring station (3) processes the photos taken by the drone and outputs monitoring data; the satellite determines whether it is necessary to release algaecides and algae toxin inhibitors based on the received monitoring data, and the drone (2) group carries the drugs and operates in real time according to the received instructions, thereby realizing dynamic monitoring and precise control of blue algae in rivers and lakes.

3. A method for dynamic monitoring and precise control of blue algae in rivers and lakes according to claim 2, characterized in that: The following steps are involved: Step 1: Use satellite (1) to control the drone to monitor the blue algae area in the river or lake in real time. The drone's image collector (2-4) obtains the blue algae area of ​​each river or lake in real time. M rivers and lakes monitor n blue algae, which are defined as M1, M2, M3, ..., M n ; Blue algae area S1, S2, S3, S4, ..., S n , where n is the number of blue algae areas in the river or 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 ratio P of the blue algae area to the river or 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; proceed 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 or 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 the data is sent to the data storage center (3-2) of the monitoring station (3); Based on formula (2), for A i，t，m1 >A0, the data processor (3-3) of the monitoring station (3) calculates the accumulated amount of blue algae exceeding the standard rate at each point W i , i = 1 to n; W i =∫(A i，t，m1 -A0)dS i Formula (2) In the formula, 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, and then proceed to step 3: X i =k1×W i Formula (3) In the formula, k1 represents the conversion coefficient of algaecide dosage, which is 0.01-0.5 μg / piece; Step 3: After the algaecide is fully reacted with the blue algae, at time t, m1', the image collectors (2-4) of each drone (2) monitor the concentration of blue algae 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 blue algae; 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 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 to n), it is determined that there is no situation where the number of cyanobacteria exceeds the limit after adding the algaecide, and step 4 is entered; otherwise, it returns to step 2 at time 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 drone monitor the area S of the blue algae 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 algae toxins at each point is B1, B2, B3, B4, ..., B n ; wherein 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 excessive algal toxin situation 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 microcystin exceeds the limit, and the drug dosing device does not need to dose the microcystin inhibitor; On the contrary, the data storage center (3-2) of the monitoring station (3) calculates the excess amount of algae toxin accumulation 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 algae in rivers and lakes; Y=k2×V Formula (5) Wherein, k2 represents the conversion coefficient of the dosage of algae toxin inhibitor, which is 1-5 mg / L for ozone, 1-5 mg / L for potassium permanganate, and 1-15 mg / L for powdered activated carbon (PAC).

4. A method for dynamic monitoring and precise control of blue algae in rivers and lakes according to claim 3, characterized in that: In step 1, the grid method is used to monitor rivers and lakes with 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, a grid line is drawn at the 1 / 3 and 2 / 3 positions in the length direction; Rule 3: If a or b>60m, a grid line is drawn every 20m starting from the midpoint of the length.

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

6. A method for dynamic monitoring and precise control of blue algae in rivers and lakes according to claim 2, 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 of algaecide dosage k1 takes the middle value k1 = 0.2-0.4μg / piece; P n When the algaecide dosage is >85%, the conversion coefficient k1 takes a higher value, k1 = 0.4-0.5 μg / piece.

7. According to claim 3, a method for dynamic monitoring and precise control of blue algae in rivers and lakes is characterized in that: In step 3, at time t,m2, compared with 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, represents 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 equation (6); And update the speed of each UAV according to formula (7); In the formula, A a+1，t，m1 is the concentration of cyanobacteria 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 a-1，t，m1 is the concentration of cyanobacteria obtained by monitoring the UAV that is close to the origin and is adjacent to UAV i in the direction of the long side a; A b+1，t，m1 is the concentration of cyanobacteria 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 concentration of cyanobacteria obtained by monitoring the UAV close to the origin and 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 drone (2).

Images