A novel digital precision control method for red imported fire ants

Abstract

This invention provides a novel digital precision control method for red imported fire ants. The method includes: S1 acquiring red imported fire ant occurrence points and aggregating all occurrence points into a set of red imported fire ant occurrence points; S2 sequentially grouping the red imported fire ant occurrence points in the set into control clusters, ensuring that red imported fire ant occurrence points that can share a single application point are grouped into one control cluster each time, until all occurrence points have been grouped; S3 applying pesticide at the centroid of each control cluster based on all control clusters collected in step S2. This method plans the optimal pesticide application point location within the red imported fire ant occurrence area, calculates the optimal pesticide dosage, controls the amount of chemical pesticides used, and reduces unnecessary pesticide waste; it precisely controls the pesticide application amount, achieving the goal of reducing pesticide use while increasing efficiency.

Description

Technical Field

[0001] This invention relates to the field of red imported fire ant control technology, and in particular to a novel digital precision control method for red imported fire ants. Background Technology

[0002] Red imported fire ants are a highly damaging invasive species. In farmland invaded by red imported fire ants, they feed on the fruits, seedlings, roots, and seeds of crops, damaging seedlings, severely affecting crop growth, causing a decline in yields, and interfering with farmers' operations, leading to abandoned farmland.

[0003] Currently, common red imported fire ant control techniques include chemical control, biological control, and physical control. Chemical control is the most common, using chemical pesticides to kill the ants. However, chemical control also has drawbacks. From a safety perspective, improper or excessive use of chemical pesticides can easily cause soil and water pollution, posing potential risks to the farmland environment. Furthermore, some pesticides can kill other insects besides ants, thus damaging the ecosystem. Because red imported fire ants have strong adaptability and reproductive capabilities, they may re-invade after chemical pesticide application, requiring repeated applications. Regarding pesticide residues, if chemical pesticides are not completely decomposed or promptly removed, residues will remain in the environment, especially in farmland. These residues may be absorbed into the food chain, potentially impacting human health.

[0004] The main problems in the current control of red imported fire ants in farmland are as follows: First, most farmers do not use standardized methods for chemical control of red imported fire ants and cannot correctly use pesticides that are effective in controlling them; second, when bitten by red imported fire ants but unable to locate the outbreak site, farmers tend to apply pesticides blindly; third, the distribution of red imported fire ants in farmland is uneven, and in order to prevent their spread, most farmers will apply pesticides in farmland areas where red imported fire ants have not yet occurred. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a novel digital precision control method for red imported fire ants, which plans the optimal location of pesticide application points in the red imported fire ant infestation area, calculates the optimal pesticide dosage, controls the amount of chemical pesticides used, and reduces unnecessary pesticide waste; and precisely controls the amount of pesticides applied to achieve the goal of reducing pesticide dosage and increasing efficacy.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] A novel digital precision control method for red imported fire ants, the method comprising:

[0008] S1 obtains the red imported fire ant spawn points and gathers all the red imported fire ant spawn points into the red imported fire ant spawn point set;

[0009] S2 sequentially groups the red imported fire ant occurrence points in the set of red imported fire ant occurrence points into control clusters, so that each time the red imported fire ant occurrence points that can share a single application point are grouped into a control cluster, until all red imported fire ant occurrence points have been grouped.

[0010] S3, based on all the control clusters collected in step S2, delivers the agent to the centroid of each control cluster.

[0011] The beneficial effects of this invention are as follows: Red imported fire ant outbreaks that can share a single application point are selected sequentially as a control cluster. All red imported fire ant outbreaks are grouped into control clusters until all outbreaks are grouped. This means that the optimal pesticide application point location within the red imported fire ant outbreak area is planned, the optimal pesticide dosage is calculated, the amount of chemical pesticides used is controlled, and unnecessary pesticide waste is reduced. Precise control of pesticide application amount achieves the goal of reducing pesticide dosage and increasing efficacy. Attached Figure Description

[0012] Figure 1 This is a flowchart of a novel digital precision control method for red imported fire ants according to an embodiment of the present invention;

[0013] Figure 2 According to the embodiment of the present invention, based on the initial occurrence point a u The first search for control cluster C u N obtained at that time k O k N k+1 A schematic diagram;

[0014] Figure 3 According to the embodiment of the present invention, based on the first search for control cluster C u The obtained N k In the second search for control cluster C u O obtained during the process k N k+1 A schematic diagram;

[0015] Figure 4 According to the second search for control cluster C in this embodiment of the invention u The obtained N k In the third search for control cluster C u O obtained during the process k N k+1 A schematic diagram;

[0016] Figure 5 According to the third search for control cluster C in this embodiment of the invention u The obtained N k In the fourth search for control cluster Cu O obtained during the process k N k+1 A schematic diagram;

[0017] Figure 6 According to the fourth search for control cluster C in this embodiment of the invention u The obtained N k The obtained control cluster C u A schematic diagram;

[0018] Figure 7 A control cluster C was found in the set of red imported fire ant occurrence points in this embodiment of the invention. u Then, the set B of uncollected red imported fire ant occurrence points was obtained. u A schematic diagram. Detailed Implementation

[0019] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0020] Please refer to Figures 1 to 7 The embodiments provided by the present invention are as follows:

[0021] A novel digital precision control method for red imported fire ants, the method comprising:

[0022] S1 obtains the red imported fire ant spawn points and gathers all the red imported fire ant spawn points into the red imported fire ant spawn point set;

[0023] S2 sequentially groups the red imported fire ant occurrence points in the set of red imported fire ant occurrence points into control clusters, so that each time the red imported fire ant occurrence points that can share a single application point are grouped into a control cluster, until all red imported fire ant occurrence points have been grouped.

[0024] S3, based on all the control clusters collected in step S2, delivers the agent to the centroid of each control cluster.

[0025] By using a computer to successively identify control clusters that can share a single application point, the area of ​​such a cluster cannot exceed the radius of a circle with the maximum predation length R of the red imported fire ants. Of course, the actual calculated control clusters may be circular or polygonal. Only one control cluster is calculated at a time, and subsequent calculations of control clusters do not include the red imported fire ant occurrence points within the already calculated control clusters. This allows for the planning of the optimal pesticide application point location within the red imported fire ant occurrence area, the calculation of the optimal pesticide dosage, control of chemical pesticide use, and reduction of unnecessary pesticide waste. Precise control of pesticide application achieves the goal of reducing pesticide dosage while increasing efficiency.

[0026] Furthermore, step S2 specifically involves:

[0027] S21, for the set of red imported fire ant occurrence points D, successively searches for a single application point P that can be shared. u The red imported fire ant outbreak points were identified and grouped into control cluster C. u Where: u = 0, 1, 2, 3, ..., m, m represents the total number of control clusters.

[0028]

[0029] Let the set of uncollected red imported fire ant occurrence points B be... u Let B represent the set of red imported fire ant outbreaks remaining after removing those already grouped into the control cluster. u Specifically:

[0030]

[0031] Where: j = 0, 1, 2, 3, ..., u;

[0032] Initially, let u = 0, then C u =0, B u =D, where C0 is an empty set and P0 is a non-existent point;

[0033] S22 Let u = u + 1, and select the set of uncollected red imported fire ant occurrence points B. u The red imported fire ant outbreak point with the smallest mid-latitude and longitude is the target cluster C for current control efforts. u The initial point of occurrence a u And find set B. u In, with the initial occurrence point a u The set of N points that occur at a distance less than the maximum feeding length R of red imported fire ants k The specific subset N k Satisfy the following formula:

[0034] N k ={q∈B u |dist(q,a u )≤R};

[0035] Where, dist(q,a) u () represents the red imported fire ant spawning point q and the initial spawning point a. u The distance between them;

[0036] S23 Find the set of red imported fire ant spawning points N. k The center of mass O k The spatial coordinates are obtained using the following method:

[0037] Assume the set of red imported fire ants is N. k If there are n occurrence points, then the subset N k The center of mass O k The coordinates are:

[0038]

[0039]

[0040] Where x is O k The longitude coordinates, y = O k Latitude coordinates;

[0041] S24 Find the set of unresolved red imported fire ant occurrence points B. u In the middle, with point O k The distance is less than the maximum feeding range R of red imported fire ants and includes the initial occurrence point a. u The set of occurrence points N k+1 The specific method is as follows:

[0042] N k+1 ={q∈B u |dist(q,O k )≤r,dist(q,a u )≤r};

[0043] Where, dist(q,O) k ) represents the red imported fire ant spawning point q and subset N. k The center of mass O k The distance between them, dist(q,a) u () represents the red imported fire ant spawning point q and the initial spawning point a. u The distance between them;

[0044] S25 Comparison of Subset N k With N k+1 The number of red imported fire ant spawning points included in the subset N, if subset N k The number of red imported fire ant occurrence points included is less than that of the subset N. k+1 The number of red imported fire ant spawning points included in N is then set to N. k =N k+1 Proceed to step S23; if subset N k The number of red imported fire ant occurrence points contained in the subset N is greater than or equal to that of the subset N. k+1 If the number of red imported fire ant spawn points is included, then continue executing the process.

[0045] S26 sets the red imported fire ant spawning points N. k Classified into prevention and control cluster C u In the middle, that is, let C u =N k Let the center of mass O K To prevent and control cluster C u Optimal dosing point P u That is, P u =O k; Prevention and control cluster C u The number of red imported fire ant outbreaks is n. u ;

[0046] S27 calculates the removal of the identified control clusters C1, C2, ..., C u The remaining set of red imported fire ant occurrence points B u At this time, B u =B u-1 -C u And determine the current set B of uncollected red imported fire ant occurrence points. u Is B an empty set? u If B is not an empty set, proceed to step S22; if B u If the set is empty, let m = u, representing the number of all control clusters.

[0047] In steps S22-S26, a control cluster C is calculated. u How to find it? Steps S21-S27 calculate the complete process of cluster aggregation for the entire prevention and control system.

[0048] For step S2, a specific embodiment is given, please refer to... Figures 2-7 As shown in the image, a small hollow dot represents a location where red imported fire ants emerge. Figure 2 Given a set D consisting of all red imported fire ant spawning points, initially let u = 0, then C u =0, B u =D;

[0049] When u = 1, refer to Figure 2 As shown, the location of the red imported fire ant outbreak with the smallest latitude and longitude is first identified as the current control cluster C to be searched for. u The initial point of occurrence a u Then, the first search for control clusters begins; specifically, the search is conducted to find clusters related to the initial occurrence point a. u The set of N points that occur at a distance less than the maximum feeding length R of red imported fire ants k Find the set of red imported fire ant spawn points N. k The center of mass O k The spatial coordinates of O at this time k Reference Figure 2 The small black dot in the middle; next, find the intersection with point O. k The distance is less than the maximum feeding range R of red imported fire ants and includes the initial occurrence point a. u The set of occurrence points N k+1 Comparison subset N k With N k+1 The number of red imported fire ant occurrence points included was found in subset N. k The number of red imported fire ant occurrence points included is less than that of the subset N. k+1The number of red imported fire ant spawning points included in N is then set to N. k =N k+1 This is equivalent to finding the new set N. k+1 Assigned to set N k ;

[0050] Next, please refer to the appendix. Figure 3 As shown, the second search for control clusters begins. Specifically, the current set of red imported fire ant spawning points N is calculated. k The center of mass O k The spatial coordinates (at which the centroid point O is obtained) k Unlike all the previous ones, please refer to the appendix for details. Figure 3 The updated centroid O is obtained by showing the two small black dots in the middle. k Next, find the sum of the sums at point O. k The distance is less than the maximum feeding range R of red imported fire ants and includes the initial occurrence point a. u The set of occurrence points N k+1 Comparison subset N k With N k+1 The number of red imported fire ant occurrence points included was found in subset N. k The number of red imported fire ant occurrence points included is less than that of the subset N. k+1 The number of red imported fire ant spawning points included in N is then set to N. k =N k+1 This is equivalent to finding the new set N. k+1 Assigned to set N k ;

[0051] Next, please refer to the appendix. Figure 4 As shown, the third search for control clusters begins; specifically, the current set of red imported fire ant spawning points N is calculated. k The center of mass O k The spatial coordinates (at which the centroid point O is obtained) k Unlike all the previous ones, please refer to the appendix for details. Figure 4 The updated centroid O is obtained by showing the three small black dots in the middle. k Next, find the sum of the sums at point O. k The distance is less than the maximum feeding range R of red imported fire ants and includes the initial occurrence point a. u The set of occurrence points N k+1 Comparison subset N k With N k+1 The number of red imported fire ant occurrence points included was found in subset N. k The number of red imported fire ant occurrence points included is less than that of the subset N. k+1 The number of red imported fire ant spawning points included in N is then set to N. k =N k+1 This is equivalent to finding the new set N. k+1 Assigned to set Nk ;

[0052] Next, please refer to the appendix. Figure 5 As shown, the fourth search for control clusters begins. Specifically, the current set of red imported fire ant spawning points N is calculated. k The center of mass O k The spatial coordinates (at which the centroid point O is obtained) k Unlike all the previous ones, please refer to the appendix for details. Figure 5 As shown by the four small black dots, the centroid O obtained in the fourth search. k The centroid O found in the third search k (Closer) to obtain the updated centroid O k Next, find the sum of the sums at point O. k The distance is less than the maximum feeding range R of red imported fire ants and includes the initial occurrence point a. u The set of occurrence points N k+1 Comparison subset N k With N k+1 The number of red imported fire ant occurrence points included was found in subset N. k The number of red imported fire ant spawning points included is equal to the subset N. k+1 The number of red imported fire ant occurrence points included is specified in the appendix. Figure 6 As shown, let C u =N k Let the center of mass O K To prevent and control cluster C u Optimal dosing point P u That is, P u =O k At this point, the search for one control cluster, C1, has been completed.

[0053] Next, calculate the set B of red imported fire ant occurrence points remaining after removing the found control cluster C1. u At this point, B1 = B0 - C1. Please refer to [the relevant documentation] for details. Figure 7 As shown, at this time Figure 7 All the hollow dots represent set B1; determine if the set of uncollected red imported fire ant occurrence points B1 is empty. If B1 is not empty, let u = u + 1, and continue searching for control cluster C2 following the process of finding control cluster C1. After finding control cluster C2, determine if B2 is empty. If it is not empty, let u = u + 1, and continue searching for the next control cluster C. u Repeat the steps of this search and control cluster process until set B is reached. u If the set is empty, the search ends.

[0054] Furthermore, the specific method for "obtaining the red imported fire ant occurrence point" in step S1 is as follows:

[0055] Images of red imported fire ant nests or red imported fire ant attractants, along with their corresponding latitude and longitude information, are collected using mobile terminal devices or drones equipped with positioning chips.

[0056] Mobile devices or drones can upload images of red imported fire ant nests or red imported fire ant attractants, along with the corresponding latitude and longitude information, to the red imported fire ant control decision-making system.

[0057] In the red imported fire ant control decision system, the latitude and longitude corresponding to the images of red imported fire ants are marked as red imported fire ant occurrence points, and the number of red imported fire ants at each occurrence point is recorded to assess the red imported fire ant occurrence severity coefficient at that occurrence point.

[0058] Searching for red imported fire ants on-site using mobile devices or drones reduces manual labor and human interference, thus improving the speed and accuracy of red imported fire ant control.

[0059] Furthermore, step S3 specifically includes:

[0060] S31 Based on the prevention and control cluster C u Given the number of red imported fire ant outbreak points, find the optimal pesticide application point P for each. u dosage of the drug (t) u The specific method is as follows:

[0061]

[0062] Where: n u Indicates control cluster C u Number of red imported fire ant outbreak points, u = 0, 1, 2, 3, ..., m, where m represents the total number of control clusters, α i denoted by , w represents the standard dosage of pesticide at a single red imported fire ant outbreak point, C0 is an empty set, and the number of red imported fire ant outbreak points in C0 is 0; the corresponding t0 of C0 is 0.

[0063] Each optimal dosing point P u and the corresponding dosage of the drug (t) u S32 collects the data into the optimal application point set P; S32 carries out pesticide control based on the optimal application point set P, at the optimal application point P. u Deploy t u Drug dosage.

[0064] The red imported fire ant occurrence severity system can determine the number of red imported fire ants at a given occurrence point. This process ensures that the amount of pesticide applied at each application point is scientific and reasonable, reducing the area of ​​pesticide application and minimizing environmental pollution.

[0065] Furthermore, step S31 also includes: calculating the total drug dosage T required for prevention and control, specifically using the following method:

[0066]

[0067] Where: m represents the total number of control clusters, t u Representative control cluster C u At the optimal dosing point P u The use of medications.

[0068] Calculate the total amount of medicine used to facilitate centralized purchasing of medicines.

[0069] Furthermore, the specific method for applying the pesticide in step S32 is as follows: using a mobile terminal or drone with positioning and navigation functions to apply the pesticide. This improves the efficiency and accuracy of on-site construction and pest control.

[0070] In summary, the novel digital precision control method for red imported fire ants provided by this invention has the following beneficial effects:

[0071] 1. Plan the optimal pesticide application points within the red imported fire ant infestation area, calculate the optimal pesticide dosage, control the amount of chemical pesticides used, and reduce unnecessary pesticide waste; accurately control the pesticide application amount to achieve the goal of reducing pesticide use and increasing efficiency.

[0072] 2. Reducing the area covered by pesticides will, to some extent, reduce environmental pollution;

[0073] 3. Improve on-site prevention and control efficiency by applying pesticides in a targeted manner.

[0074] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A novel digital precision control method for red imported fire ants, characterized in that, The method includes: S1 obtains the red imported fire ant spawn points and gathers all the red imported fire ant spawn points into the red imported fire ant spawn point set; S2 sequentially groups the red imported fire ant outbreaks in the set of outbreaks into control clusters, ensuring that outbreaks that can share a single application point are grouped into a single control cluster each time, until all red imported fire ant outbreaks have been grouped. Specifically: S21 targets the cluster of red imported fire ant outbreak points. D Searching for a shared drug delivery point step by step. P u The red imported fire ant outbreak points were collected and grouped into control clusters. C u ,in: u=0,1,2,3,...,m , m This represents the total number of control clusters. ； Gather the uncollected red imported fire ants from their occurrence points. B u This represents the set of red imported fire ant outbreaks remaining after excluding those already grouped into the control cluster. B u Specifically: ； in: j=0,1,2,3,...,u ; Initially, let u= 0, then C u = 0 , B u = D ; S22 Order u=u+ 1. Select the set of uncollected red imported fire ant occurrence points. B u The red imported fire ant outbreak point with the smallest latitude and longitude is the target cluster for current control efforts. C u initial occurrence point a u And find the set B u In, and the initial occurrence point a u The distance is less than the maximum feeding range of red imported fire ants. R The set of occurrence points N k Specific subsets N k Satisfy the following formula: ； in, Indicates the location of red imported fire ants q and the initial occurrence point a u The distance between them; S23 Find the set of spawning points for red imported fire ants. N k center of mass O k The spatial coordinates are obtained using the following method: Assume the set of red imported fire ants. N k There is n If there are several points of occurrence, then the subset N k center of mass O k The coordinates are: ； ； in, x for O k longitude coordinates y for O k Latitude coordinates; S24 Find the set of uncollected red imported fire ant occurrence points. B u In the middle, and the point O k The distance is less than the maximum feeding range of red imported fire ants. R And includes the initial occurrence point a u The set of occurrence points N k+1 The specific method is as follows: ； in, Indicates the location of red imported fire ants q and subsets N k center of mass O k The distance between them Indicates the location of red imported fire ants q and the initial occurrence point a u The distance between them; S25 Comparison Subsets N k and N k+1 The number of red imported fire ant occurrence points included in the subset, if the subset N k The number of red imported fire ant occurrence points included is less than that of the subset. N k+1 The number of red imported fire ant spawning points included in it then makes N k = N k+1 Proceed to step S23; if the subset N k The number of red imported fire ant occurrence points contained is greater than or equal to that of the subset. N k+1 If the number of red imported fire ant spawn points is included, then continue executing the process. S26 will collect the spawning points of red imported fire ants. N k Return to the prevention and control cluster C u In the middle, that is, to order C u = N k Let the center of mass be O K To prevent clusters C u Optimal dosing point P u ,Right now P u = O k ; S27 calculates the removal of the identified control clusters. C 1 ,C 2 ,...,C u The remaining red imported fire ant outbreak sites B u ,at this time B u = B u-1 - C u And determine the set of uncollected red imported fire ant occurrence points. B u Is it an empty set? If B u If it is not an empty set, proceed to step S22; if B u If it is an empty set, then let m=u ; S3, based on all the control clusters collected in step S2, delivers the agent to the centroid of each control cluster.

2. The novel digital precision control method for red imported fire ants according to claim 1, characterized in that, The specific method for "obtaining the red imported fire ant occurrence point" in step S1 is as follows: Images of red imported fire ant nests or red imported fire ant attractants, along with their corresponding latitude and longitude information, are collected using mobile terminal devices or drones equipped with positioning chips. Mobile devices or drones can upload images of red imported fire ant nests or red imported fire ant attractants, along with the corresponding latitude and longitude information, to the red imported fire ant control decision-making system. In the red imported fire ant control decision system, the latitude and longitude corresponding to the images of red imported fire ants are marked as red imported fire ant occurrence points, and the number of red imported fire ants at each occurrence point is recorded to assess the red imported fire ant occurrence severity coefficient at that occurrence point.

3. The novel digital precision control method for red imported fire ants according to claim 1, characterized in that, Step S3 is as follows: S31 Based on the prevention and control cluster C u Given the number of red imported fire ant outbreaks, find the optimal pesticide application point for each outbreak. P u Dosage of the drug t u The specific method is as follows: ； in: n u Indicates prevention and control cluster C u Number of red imported fire ant outbreak sites u=0,1,2,3,...,m , m Represents the total number of all control clusters. This indicates the severity coefficient of red imported fire ant infestation. w This indicates the standard dosage for a single red imported fire ant outbreak site; Each optimal dosing point P u and the corresponding dosage of the medicine t u Aggregate to the optimal dosing point set P middle; S32 is based on the optimal set of dosing points. P Implement pesticide control measures at the optimal application point. P u Deployment t u Drug dosage.

4. The novel digital precision control method for red imported fire ants according to claim 3, characterized in that, Step S31 also includes: calculating the total drug dosage required for prevention and control. T The specific method is as follows: ； in: m Represents the total number of all control clusters. t u Representative of prevention and control clusters C u At the optimal dosing point P u The use of medications.

5. The novel digital precision control method for red imported fire ants according to claim 3, characterized in that, The specific method for delivering the pesticide in step S32 is as follows: use a mobile terminal or drone with positioning and navigation functions to deliver the pesticide.

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