An unmanned ship-based cyanobacteria inhibitor precise spraying method and system

Abstract

The application provides a cyanobacteria inhibitor precise spraying method and system based on an unmanned ship, and belongs to the technical field of cyanobacteria treatment. The technical scheme is as follows: the spraying method comprises the following steps: collecting target water environment data and cyanobacteria concentration data, drawing a target water route planning map, dividing the target water route planning map into multiple sub-regions, judging target sub-regions that need to be sprayed with the inhibitor, planning a spraying path of the unmanned ship, determining a spraying scheme of each target sub-region, setting the spraying path of the unmanned ship and the spraying scheme of each target sub-region, and performing precise spraying of the cyanobacteria inhibitor on the target water. The system comprises an unmanned ship, a planning and drawing module, a route planning module and a spraying control module. The application realizes automatic cyanobacteria treatment by automatically performing precise spraying of the cyanobacteria inhibitor on the target water by the unmanned ship and planning the spraying path.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blue-green algae treatment, and particularly relates to a blue-green algae inhibitor precise spraying method and system based on an unmanned ship. BACKGROUND

[0002] Blue-green algae is a kind of large single-cell prokaryote capable of oxygen-producing photosynthesis. In some nutrient-rich water bodies, blue-green algae often breed massively in summer and form water bloom on the water surface. Large-scale blue-green algae outbreak is called green tide. Green tide can cause water quality deterioration and even fish death due to depletion of oxygen in water. More seriously, some species of blue-green algae can produce microcystins, and about 50% of green tides contain a large amount of microcystins. Microcystins can directly poison fish, livestock and humans and are an important inducement of liver cancer. Therefore, it is very important to treat blue-green algae. Existing blue-green algae treatment methods include biological method, chemical method and physical method. The biological method uses beneficial microorganisms to decompose organic matter and inhibit the growth and reproduction of blue-green algae (for example, the invention patent CN201610101517.9), but this method is difficult to apply to seriously polluted water areas, and the manual detection and microorganism preparation work is labor-intensive and costly. The chemical method removes blue-green algae by adding chemical agents (for example, the invention patent CN200910304291.2), but it can easily cause secondary pollution. The physical method uses manual salvage (for example, the invention patent CN200910304291.2), but the manual method is high in labor cost and low in efficiency, and can only be used when large-scale outbreak occurs. Therefore, how to provide a blue-green algae inhibitor precise spraying method and system based on an unmanned ship is a problem to be solved by those skilled in the art. SUMMARY

[0003] The present application aims to provide a blue-green algae inhibitor precise spraying method and system based on an unmanned ship, which plans the spraying path of the unmanned ship by using a path planning algorithm, sprays the blue-green algae inhibitor based on the blue-green algae concentration of the target water area and the path planning, and realizes automatic treatment of the blue-green algae in the target water area.

[0004] In order to achieve the above-mentioned application purpose, the technical scheme adopted by the present application is as follows: a blue-green algae inhibitor precise spraying method based on an unmanned ship, comprising the following steps:

[0005] S1, collecting environmental data and blue-green algae concentration data of a target water area, and drawing a route planning map of the target water area;

[0006] S2, dividing the route planning map of the target water area into a plurality of sub-areas, and determining a target sub-area needing to spray the inhibitor based on the blue-green algae concentration data in the sub-areas;

[0007] S3, planning a spraying path of the unmanned ship based on the target sub-area needing to spray the inhibitor and environmental data of the target water area;

[0008] S4, determining a spraying scheme of each target sub-area based on cyanobacteria concentration data in the target sub-area;

[0009] S5, setting the spraying path of the unmanned ship and the spraying scheme of each target sub-area to accurately spray the cyanobacteria inhibitor to the target water area.

[0010] Further, the step S1 is specifically: obtaining contour data of the target water area, setting a plurality of cyanobacteria concentration sampling points in the target water area, setting cyanobacteria concentration detection instruments and laser sensors on the unmanned ship, making the unmanned ship start from a starting sampling point, traverse all sampling points, collect cyanobacteria concentration and surrounding obstacle data of all sampling points during the navigation of the unmanned ship, and draw a route planning map of the target water area in combination with the contour data of the target water area, cyanobacteria concentration data of all sampling points and surrounding obstacle data.

[0011] Further, the step S2 is specifically: dividing the route planning map of the target water area into a plurality of grid sub-areas of equal size, marking the sub-area with obstacles as an impassable sub-area, marking the cyanobacteria concentration data of each sub-area, and dividing a sub-area with cyanobacteria concentration greater than 5×106 cell / L into a target sub-area needing to spray the inhibitor.

[0012] Further, the step S3 is specifically:

[0013] S31, planning an inhibitor spraying sequence of all target sub-areas to obtain a spraying sequence;

[0014] S32, planning a shortest spraying path of each two adjacent target sub-areas in the spraying sequence;

[0015] S33, obtaining a spraying path of the unmanned ship in combination with the spraying sequence and the shortest spraying path of each two adjacent target sub-areas in the spraying sequence.

[0016] Further, the step S31 is specifically: setting a maximum iteration number and a planning scheme number of the spraying sequence planning, constructing a spraying sequence matrix composed of all planning schemes, constructing a distance matrix between the target sub-areas, calculating the fitness of all planning schemes based on the spraying sequence matrix and the distance matrix, sorting the planning schemes from low to high according to the fitness, updating all planning schemes and recalculating the fitness of all planning schemes, sorting the planning schemes from low to high according to the new fitness, repeating the process of updating all planning schemes, calculating the fitness and sorting until the maximum iteration number is reached, and taking the planning scheme with the lowest fitness as the final spraying sequence;

[0017] The spraying sequence matrix is:

[0018]

[0019] In the formula, is the position of the unmanned ship in the jth sub-region in the ith planning scheme, i [1, n], j [1, m], n is the number of planning schemes, and m is the number of target sub-regions; each row of the spraying sequence matrix P represents a planning scheme;

[0020] The distance matrix is:

[0021]

[0022] In the formula, S jk is the distance between the jth target sub-region and the kth target sub-region;

[0023] The fitness is calculated as follows:

[0024] f = minP i (∑S i(mm) )

[0025] In the formula, P i is the spraying sequence represented by the ith planning scheme, and S i(mm) is the distance of the spraying sequence represented by the ith planning scheme.

[0026] Further, the step S32 is specifically: taking the target sub-region in sequence in two adjacent target sub-regions as a starting point, and the other target sub-region as an ending point, setting the maximum number of iterations of the shortest spraying path and the number of spraying paths, for each spraying path, calculating its transition probability and selecting its next sub-region according to the transition probability, adding the selected sub-region to the spraying path and updating the position of the unmanned ship in the current spraying path, calculating the distance of all spraying paths, if the shortest path distance of this round is less than the global shortest path distance, updating the shortest path distance of this round to the new global shortest path distance, updating the pheromone and all spraying paths, repeating the calculation of the distance of all spraying paths, the updating of the global shortest path distance, the updating of the pheromone and all spraying paths until the maximum number of iterations is reached, and the spraying path corresponding to the global shortest path distance is taken as the shortest spraying path between the two adjacent target sub-regions;

[0027] The transition probability is calculated as follows:

[0028]

[0029] In the formula, is the probability of the unmanned ship moving from sub-region i to sub-region j in the kth spraying path, G is the set of all transferable sub-regions when the unmanned ship is in a sub-region in the current spraying path, a, b, c are constants, R is the set of sub-regions that have not been visited, τ ij is the pheromone from sub-region i to sub-region j, d ij is the distance from sub-region i to sub-region j, r is a sub-region that has not been visited, τ ir is the pheromone from sub-region i to sub-region r, d ir is the distance from sub-region i to sub-region r, L k is the length of the kth spraying path.

[0030] The update of the pheromone is:

[0031]

[0032] In the formula, is the updated pheromone, ρ is the pheromone retention rate, is the pheromone of the global shortest path.

[0033] Further, the step S4 is specifically: the unmanned ship determines the amount of blue-green algae inhibitor based on the blue-green algae concentration data in the target sub-region, the speed data of the unmanned ship, and the real-time detected water depth data, and the unmanned ship realizes accurate control of the amount of blue-green algae inhibitor by controlling the spraying rate of the blue-green algae inhibitor.

[0034] Further, the step S5 further includes:

[0035] S6, set data collection time interval and spraying time interval, in one data collection time interval, for the sub-region that has not been sprayed with the inhibitor, determine the new target sub-region based on the growth curve of the blue-green algae and the spraying time interval;

[0036] S7, plan a new spraying path based on the new target sub-region and the environmental data of the target water area.

[0037] Further, the step S7 further includes:

[0038] S8, in one data collection time interval, repeat S6-S7 until the next data collection time node.

[0039] In order to better realize the above-mentioned purposes of the application, the application further provides a cyanobacteria inhibitor precise spraying system based on an unmanned ship, comprising: an unmanned ship, a planning and drawing module, a route planning module and a spraying control module, wherein the unmanned ship is provided with cyanobacteria concentration detection instruments, a laser sensor and a cyanobacteria inhibitor spraying device, the unmanned ship is used for collecting environmental data and cyanobacteria concentration data of a target water area and performing a cyanobacteria inhibitor precise spraying task, the planning and drawing module is used for drawing a route planning map of the target water area and judging target sub-areas needing to be sprayed with the inhibitor, the route planning module is used for planning a spraying path of the unmanned ship, and the spraying control module is used for determining a spraying scheme of each target sub-area.

[0040] Further, the unmanned ship is further provided with a GPS module and a depth finder, the GPS module is used for acquiring a real-time speed of the unmanned ship, and the depth finder is used for acquiring a depth of the water body, when the unmanned ship performs the spraying task, the system automatically controls the spraying system to be turned on according to parameters such as the cyanobacteria concentration, the depth of the water body and the speed of the unmanned ship, and precisely controls the amount of the cyanobacteria inhibitor.

[0041] Compared with the prior art, the application has the following beneficial effects:

[0042] 1. The application uses the unmanned ship to spray the inhibitor to treat the cyanobacteria, can save a large amount of manpower, detects the cyanobacteria concentration in the target water area, plans a spraying route and confirms a spraying scheme, can control the cyanobacteria concentration, prevents the outbreak of the cyanobacteria and avoids water pollution;

[0043] 2. The application only needs to collect environmental data and cyanobacteria concentration data once, can plan a spraying path each time within a certain time, realizes automatic cyanobacteria inhibitor spraying and cyanobacteria treatment;

[0044] 3. The application controls the amount of the cyanobacteria inhibitor in real time according to the change of the real-time detected cyanobacteria concentration data, can effectively kill the cyanobacteria and reduce the amount and cost of the cyanobacteria inhibitor;

[0045] 4. The application can monitor the change of the cyanobacteria concentration of the water body before the cyanobacteria outbreak, sprays the cyanobacteria inhibitor in advance, kills the cyanobacteria in the water body in advance and avoids large-scale outbreak of the cyanobacteria. BRIEF DESCRIPTION OF DRAWINGS

[0046] The accompanying drawings are included to provide a further understanding of the application, and constitute a part of the specification, illustrate the application together with the embodiments thereof, and explain the application without imposing a limitation on the application.

[0047] Figure 1 The application provides a cyanobacteria inhibitor precise spraying method.

[0048] Figure 2 The application provides a spraying path planning flowchart.

[0049] Figure 3 The water area cyanobacteria concentration distribution diagram after spraying for three days in the embodiment 2 of the present application.

[0050] Figure 4 The water area cyanobacteria concentration distribution diagram after spraying for three days in the embodiment 2 of the present application.

[0051] Figure 5 The cyanobacteria concentration distribution diagram before spraying in the embodiment 3 of the present application on June 7, 2024 in a certain area.

[0052] Figure 6 The cyanobacteria concentration distribution diagram after unmanned ship completes automatic spraying of cyanobacteria inhibitor in the same area on June 8, 2024. Figure 5 The cyanobacteria concentration distribution diagram after unmanned ship completes automatic spraying of cyanobacteria inhibitor in the same area on June 8, 2024. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical scheme and advantages of the present application more clear and obvious, the present application will be further described in detail below in combination with the drawings and embodiments. Of course, the specific embodiments described here are only used to explain the present application, and are not used to limit the present application.

[0054] Embodiment 1

[0055] Referring to Figure 1 and Figure 2 , the embodiment discloses a kind of based on unmanned ship's cyanobacteria inhibitor accurate spraying method, as shown in Figure 1 , comprising the following steps:

[0056] S1, the environmental data of target water area and cyanobacteria concentration data are collected, and the route planning diagram of target water area is drawn;

[0057] S2, the route planning diagram of target water area is divided into multiple sub-regions, and the target sub-region needing to spray inhibitor is judged based on cyanobacteria concentration data in sub-region;

[0058] S3, the spraying path of unmanned ship is planned based on the target sub-region needing to spray inhibitor and the environmental data of target water area;

[0059] S4, the spraying scheme of each target sub-region is determined based on cyanobacteria concentration data in target sub-region;

[0060] S5, the spraying path of unmanned ship and the spraying scheme of each target sub-region are set, and the accurate spraying of cyanobacteria inhibitor to target water area is carried out.

[0061] Preferably, S1 is specifically: acquiring contour data of the target water area, setting a plurality of cyanobacteria concentration sampling points in the target water area, setting a cyanobacteria concentration detection instrument and a laser sensor on the unmanned ship, making the unmanned ship start from a starting sampling point, traversing all sampling points, collecting cyanobacteria concentration and surrounding obstacle data of all sampling points during the unmanned ship sailing, and drawing a route planning map of the target water area in combination with the contour data of the target water area, cyanobacteria concentration data of all sampling points and surrounding obstacle data.

[0062] Preferably, S2 is specifically: dividing the route planning map of the target water area into a plurality of grid sub-areas of equal size, marking sub-areas with obstacles as impassable sub-areas, marking cyanobacteria concentration data of each sub-area, and dividing a sub-area with cyanobacteria concentration greater than 5*10 6 cell / L into a target sub-area requiring inhibitor spraying.

[0063] Preferably, S3 is specifically:

[0064] S31, planning an inhibitor spraying sequence of all target sub-areas to obtain a spraying sequence;

[0065] S32, planning a shortest spraying path of each two adjacent target sub-areas in the spraying sequence;

[0066] S33, obtaining a spraying path of the unmanned ship in combination with the spraying sequence and the shortest spraying path of each two adjacent target sub-areas in the spraying sequence.

[0067] Preferably, S31 is specifically: setting a maximum iteration number and a planning scheme number of the spraying sequence planning, constructing a spraying sequence matrix composed of all planning schemes, constructing a distance matrix between target sub-areas, calculating fitness of all planning schemes based on the spraying sequence matrix and the distance matrix, sorting the planning schemes from low to high according to the fitness, updating all planning schemes and recalculating fitness of all planning schemes, sorting the planning schemes from low to high according to the new fitness, repeating the process of updating all planning schemes, calculating fitness and sorting until the maximum iteration number is reached, and taking the planning scheme with the lowest fitness as the final spraying sequence.

[0068] In the embodiment of the application, the spraying sequence matrix is:

[0069]

[0070] In the formula, is a position of the unmanned ship in the jth sub-area in the ith planning scheme, i∈[1,n], j∈[1,m], n is the number of planning schemes, and m is the number of target sub-areas; each row of the spraying sequence matrix P represents a planning scheme.

[0071] The distance matrix is:

[0072]

[0073] In the formula, S jk is the distance between the jth target sub-region and the kth target sub-region;

[0074] The calculation of the fitness is specifically:

[0075] f = min P i (∑S i(mm) )

[0076] In the formula, P i is the spraying sequence represented by the ith planning scheme, and S i(mm) is the distance of the spraying sequence represented by the ith planning scheme.

[0077] Preferably, as Figure 2 shown, S32 specifically includes the following steps: taking a target sub-region in front in sequence in two adjacent target sub-regions as a starting point and taking the other target sub-region as an ending point, setting the maximum number of iterations of the shortest spraying path and the number of spraying paths, for each spraying path, calculating the transition probability thereof and selecting the next sub-region according to the transition probability, adding the selected sub-region to the spraying path and updating the position of the unmanned ship in the current spraying path, calculating the distance of all spraying paths, if the distance of the shortest path in the current round is less than the distance of the global shortest path, updating the distance of the shortest path in the current round as the new distance of the global shortest path, updating the pheromone and all spraying paths, repeating the calculation of the distance of all spraying paths, the updating of the distance of the global shortest path, the updating of the pheromone and all spraying paths until the maximum number of iterations is reached, and taking the spraying path corresponding to the distance of the global shortest path as the shortest spraying path between the two adjacent target sub-regions.

[0078] In the embodiment of the application, the calculation of the transition probability is as follows:

[0079]

[0080] In the formula, P is the probability of the unmanned ship in the kth spraying path from the sub-region i to the sub-region j, G is the set of all transferable sub-regions when the unmanned ship is in a sub-region in the current spraying path, a, b and c are constants, R is the set of sub-regions that have not been visited, τ ij is the pheromone from the sub-region i to the sub-region j, d ij is the distance from the sub-region i to the sub-region j, τ ir is the pheromone from the sub-region i to the sub-region r, d ir is the distance from the sub-region i to the sub-region r, and L kLength of the kth spraying path.

[0081] The update of pheromone is:

[0082]

[0083] In the formula, is the updated pheromone, and p is the pheromone retention rate, is the pheromone of the global shortest path, and in the embodiment of the application, only the pheromone of the global shortest path is updated.

[0084] Preferably, S4 specifically is: the unmanned ship determines the amount of blue-green algae inhibitor based on the blue-green algae concentration data in the target sub-region, the speed data of the unmanned ship, and the real-time detected water depth data, and the unmanned ship realizes accurate control of the amount of blue-green algae inhibitor by controlling the spraying rate of the blue-green algae inhibitor.

[0085] In the embodiment of the application, the blue-green algae inhibitor is carried in the middle warehouse of the unmanned ship, the bottom of the middle warehouse is provided with a screw structure, the rotation speed of the screw is controlled by a motor, the blue-green algae inhibitor falls into the lowered warehouse by its own gravity, the left side of the warehouse is a water inlet, and the right side is a water outlet, the water inlet is connected with a water pump, and the right water outlet is connected to the position of the propeller of the unmanned ship; when the screw rotates, the water pump is automatically opened, the blue-green algae inhibitor in the warehouse is mixed with water, and then flows out from the right water outlet, the outflow of the powder-liquid mixture is uniformly diffused into the water body by the thrust and stirring capacity of the propeller of the unmanned ship during navigation, the rotation speed of the screw is automatically controlled by real-time monitoring of the algal protein concentration, the speed of the unmanned ship obtained by GPS, and the water depth data, so as to accurately control the amount of blue-green algae inhibitor; when the area where the blue-green algae concentration exceeds the threshold value is reached, the screw rotation is started to push the blue-green algae inhibitor, and when the area is left, the screw rotation is stopped; the higher the blue-green algae concentration, the greater the rotation speed of the screw, and the greater the amount of blue-green algae inhibitor;

[0086] In the embodiment of the application, the spraying flow range of the inhibitor is 0.5-3 g / m 3 When the blue-green algae concentration is 5x10 6 cell / L, the spraying flow is 0.5 g / m 3 When the blue-green algae concentration is greater than or equal to 10 7 cell / L, the spraying flow is 3 g / m 3 Based on the spraying flow, the speed of the unmanned ship, and the water depth, the current rotation speed of the screw is obtained.

[0087] Preferably, S5 further includes:

[0088] S6, set a data collection time interval and a spraying time interval, in a data collection time interval, for a sub-region which has not been sprayed with the inhibitor, determine a new target sub-region based on a growth curve of the cyanobacteria and the spraying time interval;

[0089] S7, plan a new spraying path based on the new target sub-region and the environmental data of the target water area.

[0090] In the embodiment of the present application, the cyanobacteria concentration in the sub-region at the next spraying time node can be calculated according to the growth curve of the cyanobacteria and the spraying time interval, and based on the concentration, it is determined whether the sub-region needs to be sprayed with the inhibitor and the inhibitor spraying scheme is determined.

[0091] Preferably, S7 further comprises:

[0092] S8, in a data collection time interval, repeat S6-S7 until the next data collection time node.

[0093] In the embodiment of the present application, a data collection time interval includes a plurality of spraying time intervals, and after a data collection time interval ends, the environmental data and the cyanobacteria concentration data of the target water area are re-collected.

[0094] Corresponding to the method, Figure 1 Corresponding to the method, the embodiment of the present application also discloses a cyanobacteria inhibitor precise spraying system based on an unmanned ship, which executes the cyanobacteria inhibitor precise spraying method based on the unmanned ship, and comprises an unmanned ship, a planning and drawing module, a route planning module and a spraying control module, wherein the unmanned ship is provided with cyanobacteria concentration detection instruments, a laser sensor and a cyanobacteria inhibitor spraying device, the unmanned ship is used to collect the environmental data and the cyanobacteria concentration data of the target water area and execute the cyanobacteria inhibitor precise spraying task, the planning and drawing module is used to draw a route planning map of the target water area and determine a target sub-region which needs to be sprayed with the inhibitor, the route planning module is used to plan a spraying path of the unmanned ship, and the spraying control module is used to determine a spraying scheme of each target sub-region.

[0095] Preferably, in the embodiment of the present application, the unmanned ship is further provided with a GPS module and a depth finder, the GPS module is used to obtain the real-time speed of the unmanned ship, and the depth finder is used to obtain the depth of the water body, when the unmanned ship executes the spraying task, the system automatically controls the opening of the spraying system according to the cyanobacteria concentration, the depth of the water body, the speed of the unmanned ship and other parameters, and accurately controls the amount of the cyanobacteria inhibitor.

[0096] Embodiment 2

[0097] In a certain water area, cyanobacteria inhibitor spraying work is carried out, and the cyanobacteria concentration and distribution before the cyanobacteria inhibitor spraying are as follows Figure 3The red area indicates the cyanobacteria outbreak area, and the red degree indicates the cyanobacteria concentration. It can be seen that the cyanobacteria concentration in the area is significantly reduced after spraying for three days, see Figure 4 .

[0098] Example 3

[0099] See Figure 5 is the cyanobacteria concentration distribution map before spraying in a certain area on June 7, 2024.

[0100] See Figure 6 is the cyanobacteria concentration distribution map after the unmanned ship completes the automatic spraying of cyanobacteria inhibitors in the same area on June 8, 2024.

[0101] It can be seen that the cyanobacteria concentration in most of the area has been greatly reduced after 24 hours.

[0102] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for precise spraying of cyanobacteria inhibitor based on unmanned ship, characterized in that, The method comprises the following steps: S1, collecting environmental data and cyanobacteria concentration data of a target water area, and drawing a route planning map of the target water area; S2, dividing the route planning map of the target water area into a plurality of sub-areas, and determining target sub-areas needing to be sprayed with an inhibitor based on cyanobacteria concentration data in the sub-areas; S3, planning a spraying path of an unmanned ship based on the target sub-areas needing to be sprayed with the inhibitor and the environmental data of the target water area; The step S3 comprises the following steps: S31, planning an inhibitor spraying sequence of all the target sub-areas to obtain a spraying sequence; The step S31 specifically comprises: setting a maximum iteration number and a planning scheme number of the spraying sequence planning, constructing a spraying sequence matrix composed of all the planning schemes, constructing a distance matrix between the target sub-areas, calculating fitness of all the planning schemes based on the spraying sequence matrix and the distance matrix, sorting the planning schemes from low to high according to the fitness, updating all the planning schemes and recalculating the fitness of all the planning schemes, sorting the planning schemes from low to high according to the new fitness, repeating the process of updating all the planning schemes, calculating the fitness and sorting until the maximum iteration number is reached, and taking the planning scheme with the lowest fitness as the final spraying sequence; The spraying sequence matrix is: ； In the formula, is the position of the unmanned ship in the i-th planning scheme in the j-th sub-region, , n is the number of planning schemes, and m is the number of target sub-regions; each row of the spraying sequence matrix P represents a planning scheme; The distance matrix is: ； In the formula, is the distance between the jth target sub-region and the kth target sub-region; The fitness is calculated as: ； wherein is the distance of the spray sequence represented by the i-th planning scheme, is the distance of the spray sequence represented by the i-th planning scheme, S32, planning a shortest spraying path between each two adjacent target sub-areas in the spraying sequence; S33, combining the spraying sequence and the shortest spraying path between each two adjacent target sub-areas in the spraying sequence to obtain the spraying path of the unmanned ship; S4, determining a spraying scheme of each target sub-area based on cyanobacteria concentration data in the target sub-area; S5, setting the spraying path of the unmanned ship and the spraying scheme of each target sub-area, and performing accurate spraying of cyanobacteria inhibitor on the target water area; S6, setting a data collection time interval and a spraying time interval, and in one data collection time interval, determining new target sub-areas based on a growth curve of cyanobacteria and the spraying time interval for sub-areas not sprayed with the inhibitor; S7, planning a new spraying path based on the new target sub-areas and the environmental data of the target water area; S8, in one data collection time interval, repeating steps S6 to S7 until a next data collection time node. 2.The method of claim 1, wherein the method further comprises: determining a position of the unmanned ship based on a global positioning system (GPS) signal; and determining a position of the unmanned ship based on a sensor signal. The step S1 specifically comprises: obtaining contour data of the target water area, setting a plurality of cyanobacteria concentration sampling points in the target water area, setting cyanobacteria concentration detection instruments and laser sensors on the unmanned ship, making the unmanned ship start from a starting sampling point, traversing all the sampling points, collecting cyanobacteria concentration and surrounding obstacle data of all the sampling points during the navigation of the unmanned ship, and drawing a route planning map of the target water area based on the contour data of the target water area, the cyanobacteria concentration data of all the sampling points and the surrounding obstacle data. 3.The method of claim 1, wherein the method further comprises: determining a position of the unmanned ship based on a global positioning system (GPS) signal; and determining a position of the unmanned ship based on a sensor signal. The step S2 is specifically: dividing the route planning map of the target water area into a plurality of grid sub-regions of equal size, marking the sub-regions with obstacles as impassable sub-regions, marking each sub-region with its cyanobacteria concentration data, and when the cyanobacteria concentration in a sub-region is greater than , dividing it into a target sub-region requiring spraying of the inhibitor.

4. The method for precise spraying of cyanobacteria inhibitors based on unmanned vessels according to claim 1, characterized in that, The step S32 specifically comprises: taking a target sub-region in front of two adjacent target sub-regions as a starting point, taking another target sub-region as an ending point, setting a maximum number of iterations of the shortest spraying path and a number of spraying paths, for each spraying path, calculating a transition probability thereof and selecting a next sub-region thereof according to the transition probability, adding the selected sub-region to the spraying path and updating a position of the unmanned ship in the current spraying path, calculating distances of all spraying paths, if a shortest path distance of the current round is less than a global shortest path distance, updating the shortest path distance of the current round as a new global shortest path distance, updating pheromones and all spraying paths, repeating the calculation of the distances of all spraying paths, the updating of the global shortest path distance, and the updating of the pheromones and all spraying paths until the maximum number of iterations is reached, and taking a spraying path corresponding to the global shortest path distance as the shortest spraying path between the two adjacent target sub-regions; The calculation of the transition probability is as follows: ； ； wherein, is the probability of the unmanned ship moving from sub-area i to sub-area j in the kth spraying path, G is the union set of all transferable sub-areas when the unmanned ship is in a sub-area in the current spraying path, a, b, and c are constants, and R is the union set of sub-areas that have not been visited, is the pheromone from sub-area i to sub-area j, is the distance from sub-area i to sub-area j, and r is a sub-area that has not been visited, is the pheromone from sub-area i to sub-area r, is the distance from sub-area i to sub-area r, is the length of the kth spraying path. The updating of the pheromones is as follows: ； In the formula, is the updated pheromone, is the pheromone retention rate, is the pheromone of the global shortest path.

5. The method of claim 1, wherein the method further comprises: The step S4 specifically comprises: the unmanned ship determining a dosage of the blue-green algae inhibitor based on blue-green algae concentration data in the target sub-region, ship speed data, and real-time detected water depth data, and the unmanned ship realizing accurate control of the dosage of the blue-green algae inhibitor by controlling a spraying rate of the blue-green algae inhibitor.

6. An unmanned ship-based cyanobacteria suppressant precision spraying system, characterized by, The unmanned ship-based blue-green algae inhibitor accurate spraying method according to any one of claims 1-5 comprises: an unmanned ship, a planning and drawing module, a route planning module, and a spraying control module. The unmanned ship is provided with blue-green algae concentration detection instruments, a laser sensor, and a blue-green algae inhibitor spraying device. The unmanned ship is used to collect environmental data and blue-green algae concentration data of a target water area and perform a blue-green algae inhibitor accurate spraying task. The planning and drawing module is used to draw a route planning map of the target water area and determine target sub-regions requiring spraying of the inhibitor. The route planning module is used to plan spraying paths of the unmanned ship, and the spraying control module is used to determine a spraying scheme for each target sub-region.

7. The unmanned-ship-based cyanobacteria suppressor precise spraying system according to claim 6, wherein, The unmanned ship is further provided with a GPS module and a depth finder, the GPS module is used to obtain a real-time speed of the unmanned ship, and the depth finder is used to obtain a depth of the water body. When the unmanned ship performs a spraying task, the system automatically controls the spraying system to be turned on according to parameters such as the blue-green algae concentration, the water depth, and the ship speed, and accurately controls the dosage of the blue-green algae inhibitor.

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