Solenopsis invicta living area prediction method and system based on artificial intelligence

Abstract

The invention belongs to the field of invasion species prevention and control, and relates to a solenopsis invicta living area prediction technology, in particular to a solenopsis invicta living area prediction method and system based on artificial intelligence, and the method specifically comprises the steps: marking a solenopsis invicta adjacent area as a prediction area, and determining a survival factor 1 through environment data; determining a survival factor 2 through the soil type, the vegetation coverage rate and the landform, and integrating the determined survival factor 1 and the determined survival factor 2 into a total survival factor; and determining a prediction area with the solenopsis invicta according to a flood diffusion factor obtained by combining the total survival factor with the flood flow and the start time and a marriage flight diffusion factor obtained based on the start time of marriage flight diffusion of the solenopsis invicta. According to the method, passive marking of the solenopsis invicta living area is changed into active prediction, the problems that existing marking of the solenopsis invicta living area is delayed, and the diffusion condition of the solenopsis invicta living area under the natural condition is difficult to predict and recognize are solved, and the capability of human beings for preventing and controlling the solenopsis invicta is improved.

Description

Technical Field

[0001] This invention belongs to the field of invasive species control and relates to red imported fire ant habitat prediction technology, specifically an artificial intelligence-based method and system for predicting red imported fire ant habitats. Background Technology

[0002] Red imported fire ants are a highly dangerous invasive species native to South America and are now recognized as one of the world's 100 most dangerous invasive species. They cause severe damage to ecosystems, not only feeding on crop seeds, fruits, and buds, but also interfering with agricultural production and damaging crop root systems. Furthermore, they alter the physical and chemical properties of topsoil, affecting soil ecological balance. They also attack small animals and interfere with electrical equipment, further exacerbating their negative impacts on the environment and human activities. Therefore, identifying the habitats of red imported fire ants is crucial for their control and research.

[0003] Currently, most AI-based methods and systems for predicting the habitat of red imported fire ants rely on manual identification and re-marking to identify and mark their habitats. However, this passive approach is significantly lagging and makes it difficult to predict the spread of red imported fire ants under natural conditions. Consequently, the control of red imported fire ants cannot keep up with their spread, causing serious harm to the ecosystem.

[0004] Therefore, there is an urgent need for a predictive method and system for timely prediction and identification of the living areas of red imported fire ants in order to solve the above-mentioned technical problems. Summary of the Invention

[0005] This invention aims to address at least one of the technical problems existing in the prior art. To this end, this invention proposes an artificial intelligence-based method and system for predicting the habitat of red imported fire ants. This addresses the technical problem that existing methods for marking red imported fire ant habitats involve discovery followed by re-marking, which results in a delay in marking and makes it difficult to predict and identify the spread of red imported fire ants under natural conditions. This invention marks the neighboring areas of red imported fire ants as the prediction area, acquiring environmental data, basic data, and spread data. Survival factor one is determined using the environmental data, and survival factor two is determined using the basic data. These two factors are then integrated into a total survival factor. This total survival factor is combined with a flood spread factor determined based on the flood flow and start time, and a nuptial flight spread factor determined based on the start time of the red imported fire ant nuptial flight spread to determine the predicted area where red imported fire ants exist, thus solving the aforementioned problem.

[0006] To achieve the above objectives, a first aspect of the present invention provides an artificial intelligence-based method for predicting the habitat area of ​​red imported fire ants, comprising:

[0007] The areas adjacent to the appearance of red imported fire ants were marked as prediction areas. Environmental data, basic data and diffusion data were obtained for each prediction area. The environmental data included temperature, humidity and rainfall; the basic data included soil type, vegetation cover and topography; and the diffusion data included the flow rate and start time of floods and the start time of red imported fire ant nuptial flight diffusion.

[0008] Based on environmental data, the survival factor 1 of red imported fire ants in each prediction area was determined, and based on basic data, the survival factor 2 of red imported fire ants in each prediction area was determined; the survival factor 1 and the survival factor 2 were integrated into the total survival factor.

[0009] The flood diffusion factor for each predicted area at the current time is determined based on the flow rate and start time of the flood. The nuptial flight diffusion factor for each predicted area at the current time is determined based on the start time of the red imported fire ant nuptial flight diffusion. The predicted areas where red imported fire ants exist are determined based on the total survival factor, flood diffusion factor, and nuptial flight diffusion factor.

[0010] Preferably, marking the area adjacent to the occurrence of red imported fire ants as the predicted area includes:

[0011] S11. Extract the location of red imported fire ant nests from the database, and mark the area covered by the foraging distance as the radius of the location as the area where red imported fire ants appear. The foraging distance is the foraging radius of the worker ants in the red imported fire ant population.

[0012] S12. Using the location where the red imported fire ant nest appears as the center and the distance between nests as the radius, draw a line to obtain a mapped nest circle; using the due south direction in the mapped nest circle as the starting point and the distance between nests as the distance, set several predicted nests on the mapped nest circle in a counterclockwise rotation; using the predicted nests as the center and the foraging distance as the radius, mark the area covered as the predicted area, where the distance between nests is the preset standard distance between two red imported fire ant nests.

[0013] Preferably, the acquisition of environmental data, basic data, and diffusion data for each prediction area includes:

[0014] The system extracts historical temperature, humidity, and annual rainfall data for a predicted area over several days and several years from the weather forecast system. It then calculates the predicted temperature by weighting the maximum, minimum, and average values ​​of the historical temperature, humidity, and annual rainfall data. Finally, it calculates the predicted rainfall by weighting the maximum, minimum, and average values ​​of the historical humidity.

[0015] Soil type, vegetation cover and topography of each prediction area were extracted from the database; the flow rate and start time of the most recent flood in each prediction area were extracted from the database, as well as the start time of several nuptial flight dispersals of red imported fire ants in the history of areas adjacent to each prediction area where red imported fire ants appeared.

[0016] Preferably, the step of determining the survival factor of red imported fire ants in each predicted area based on environmental data includes:

[0017] Temperature WD of each prediction region is extracted sequentially. i Humidity SD i and rainfall YL i Where i is the number of the predicted region;

[0018] When temperature WD is present i Temperatures exceeding the survival range of red imported fire ants, or humidity exceeding the limit. i The humidity level is outside the survival range of red imported fire ants, or there is rainfall. i When the rainfall exceeds the range where red imported fire ants can survive, the current predicted area will be marked as an area where red imported fire ants do not exist;

[0019] When there is no temperature WD i Exceeding the survival temperature range and lacking humidity SD i Exceeding the survival humidity range and with no rainfall YL i When the rainfall exceeds the survival range, based on the temperature WD i Humidity SD i and rainfall YL i Determine the survival factor C1 of red imported fire ants in the current prediction area. i ;

[0020] The survival factor C1 i Satisfies formula (1):

[0021]

[0022] Where ZWD is the median value of the survival temperature range, ZSD is the median value of the survival humidity range, and ZYL is the median value of the survival rainfall range; α1, α2 and α3 are all artificially set proportional adjustment coefficients, and α1+α2+α3=1, α1>α2>α3.

[0023] Preferably, the step of determining the second survival factor of red imported fire ants in each prediction area based on basic data specifically involves: sequentially inputting the soil type, vegetation coverage, and topography of each prediction area into the survival assessment model to obtain the second survival factor corresponding to each prediction area, wherein the survival assessment model construction steps include:

[0024] S21. Extract soil type, vegetation coverage, topography and survival factor 2 from historical reference data. The historical reference data includes several soil types, vegetation coverage, topography and survival factor 2 set by experts based on soil type, vegetation coverage and topography.

[0025] S22. Integrate soil type, vegetation coverage, topography, and survival factor 2 into several sets of training data and test data; use the training data to train the artificial intelligence model, use the test data to test the trained artificial intelligence model, and adjust the artificial intelligence model according to the test results; finally, obtain a survival assessment model with soil type, vegetation coverage, and topography as input and survival factor 2 as output, where the artificial intelligence model includes a BP neural network model and an RBF neural network model.

[0026] Preferably, the integration of survival factor one and survival factor two into a total survival factor includes:

[0027] Extracting survival factor C1 i And survival factor 2 C2 i Based on survival factor C1 i And survival factor 2 C2 i Determine the total survival factor ZC i The total survival factor ZC i Satisfies formula (2):

[0028] ZC i =β1×C1 i +β2×C2 i (2);

[0029] Wherein, β1 and β2 are both manually set proportional adjustment coefficients with values ​​of [0,1], and β1+β2=1;

[0030] When the total survival factor ZC of the predicted region i When the value is greater than 1, the total survival factor ZC of the current prediction region is increased. i Set to 1.

[0031] Preferably, determining the flood diffusion factor for each predicted area at the current time based on the flood discharge and start time includes:

[0032] Obtain the flow rate HQ of the most recent flood in each predicted area sequentially. i and start time HT i Determine whether there are areas upstream of the predicted area that have already developed red imported fire ants after the predicted area is affected by flooding from upstream; if yes, determine the distance L between the current predicted area and the upstream area where red imported fire ants have already developed. j and the flood flow rate HQi and start time HT i Determine the flood diffusion factor HZ for the current forecast area at the current time. i The flood diffusion factor HZ i If the conditions are met (3), then the flood diffusion factor HZ for the current predicted area in the current time period is used. i Set to 0;

[0033] Formula (3):

[0034]

[0035] Where j is the distance L between the current predicted area and the upstream area where red imported fire ants exist. j The number is given, and the value of j ranges from [1, n], where n is the distance from L. j The maximum value of the number; BL is the standard soaking distance set by the human, BHQ is the standard flow rate set by the human, DT is the current time, BC is the standard duration between the time when red imported fire ants begin to reproduce after the flood spread and the time when the flood occurs; δ1, δ2 and δ3 are all proportional adjustment coefficients set by the human with a value range of [0,1], δ1+δ2+δ3=1, and δ2>δ1>δ3;

[0036] When the flood diffusion factor HZ of the predicted area i When the flood diffusion factor HZ in the current predicted area is greater than the flood diffusion threshold, the flood diffusion factor HZ in the current predicted area will be adjusted. i The value is taken as the flood diffusion threshold; where the flood diffusion threshold is obtained through empirical setting.

[0037] Preferably, the step of determining the nuptial flight dispersal factor for each predicted region at the current time based on the start time of the red imported fire ant nuptial flight dispersal includes:

[0038] Extract the start time (RT) of several nuptial flight dispersals of red imported fire ants from the historical data of areas adjacent to the predicted regions where red imported fire ants have appeared. r The starting time of the red imported fire ant nuptial flight dispersal RT r If the time difference with the current time is less than the time threshold, then the wedding flight diffusion factor RZ for the current prediction region will be adjusted. i Set to 0; where r is the start time RT of the red imported fire ant nuptial flight dispersal. r The number is given, and the value of r is in the range of [0, z], where z is the start time RT of the red imported fire ant nuptial flight dispersal. r The maximum value of the number;

[0039] The start time of the red imported fire ant nuptial flight dispersal RT r When the time difference with the current time is not less than a time threshold, based on time RT rDetermine the diffusion factor RZ for the current prediction region. i The time threshold is determined empirically.

[0040] The wedding flight diffusion factor RZ i Satisfies formula (4):

[0041]

[0042] Where DT is the current time and DC is a manually set reference duration;

[0043] When the prediction area's wedding flight diffusion factor RZ i When the value is greater than 1, the wedding flight diffusion factor RZ in the current prediction region will be adjusted. i Set to 1.

[0044] Preferably, the determination of the predicted area where red imported fire ants exist based on the total survival factor, flood diffusion factor, and nuptial flight diffusion factor includes:

[0045] Extract the total survival factor ZC for each prediction region sequentially. i Flood diffusion factor HZ i and the diffusion factor RZ i Based on the total survival factor ZC i The flood diffusion factor HZ i and the aforementioned wedding flight diffusion factor RZ i Determine the presence factor CY of red imported fire ants in the current prediction area. i The existence factor CY i Satisfies formula (5):

[0046] CY i =μ1×RZ i +μ2×ZC i +μ3×HZ i (5);

[0047] Where μ1, μ2 and μ3 are all manually set proportional adjustment coefficients with a value range of [0,1], and μ1+μ2+μ3=1;

[0048] If the presence factor CY of red imported fire ants in the current predicted area i If the value exceeds the existence threshold, the current predicted area is marked as an area where red imported fire ants exist; the existence threshold is determined empirically.

[0049] A second aspect of the present invention provides an artificial intelligence-based red imported fire ant habitat prediction system, comprising: a survival identification module, and a data collection module, an area prediction module, and a database connected to the survival identification module;

[0050] Data collection module: Marks areas near where red imported fire ants appear as prediction areas, and acquires environmental data, basic data and diffusion data for each prediction area. The environmental data includes temperature, humidity and rainfall; the basic data includes soil type, vegetation coverage and topography; the diffusion data includes the flow rate and start time of floods and the start time of red imported fire ant nuptial flight diffusion.

[0051] Survival identification module: Based on environmental data, determine the first survival factor of red imported fire ants in each prediction area; based on basic data, determine the second survival factor of red imported fire ants in each prediction area; integrate survival factor one and survival factor two into the total survival factor.

[0052] Regional Prediction Module: Determines the flood diffusion factor for each prediction region at the current time based on the flood flow and start time, determines the nuptial flight diffusion factor for each prediction region at the current time based on the start time of the red imported fire ant nuptial flight diffusion, and determines the prediction region where red imported fire ants exist based on the total survival factor, flood diffusion factor, and nuptial flight diffusion factor.

[0053] Database: Used to store data.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] 1. This invention marks areas near where red imported fire ants are found as prediction areas, and acquires environmental data, basic data, and diffusion data for each prediction area. Based on the environmental data, it determines the first survival factor of red imported fire ants in each prediction area, and based on the basic data, it determines the second survival factor. The first and second survival factors are integrated into a total survival factor. Based on the flood flow and start time, it determines the flood diffusion factor for each prediction area at the current time; based on the start time of the red imported fire ant nuptial flight diffusion, it determines the nuptial flight diffusion factor for each prediction area at the current time. Based on the total survival factor, flood diffusion factor, and nuptial flight diffusion factor, it determines the prediction areas where red imported fire ants exist. This invention solves the technical problem that existing methods of marking red imported fire ant habitats involve discovery followed by marking, which causes a delay in marking and makes it difficult to predict and identify the diffusion of red imported fire ant habitats under natural conditions. This invention transforms the passive marking of red imported fire ant habitats into active prediction, improving human capabilities for red imported fire ant control.

[0056] 2. Calculating the survival factors of red imported fire ants in each predicted region is a crucial step in this invention, quantifying the ecological adaptability of red imported fire ants and environmental variables. Its core value lies in systematically assessing the survival potential of red imported fire ants in different regions through scientific modeling and data-driven methods. This process not only deepens our understanding of the distribution patterns of red imported fire ants but also provides actionable decision-making support for invasion control, ecological risk prediction, and resource management. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a schematic diagram of the operation steps of the present invention;

[0059] Figure 2 This is a schematic diagram illustrating the operation steps for marking the predicted region in this invention;

[0060] Figure 3 This is a schematic diagram of the marker prediction region of the present invention;

[0061] Figure 4 This is a schematic diagram of the system modules of the present invention. Detailed Implementation

[0062] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] Please see Figure 1 The first aspect of this invention provides a method for predicting the habitat of red imported fire ants based on artificial intelligence, comprising:

[0064] The areas adjacent to the appearance of red imported fire ants were marked as prediction areas. Environmental data, basic data and diffusion data were obtained for each prediction area. The environmental data included temperature, humidity and rainfall; the basic data included soil type, vegetation cover and topography; and the diffusion data included the flow rate and start time of floods and the start time of red imported fire ant nuptial flight diffusion.

[0065] Based on environmental data, the survival factor 1 of red imported fire ants in each prediction area was determined. Based on basic data, the survival factor 2 of red imported fire ants in each prediction area was determined. The survival factors 1 and 2 were then integrated into the total survival factor.

[0066] The flood diffusion factor for each predicted area at the current time is determined based on the flow rate and start time of the flood. The nuptial flight diffusion factor for each predicted area at the current time is determined based on the start time of the red imported fire ant nuptial flight diffusion. The predicted areas where red imported fire ants exist are determined based on the total survival factor, flood diffusion factor, and nuptial flight diffusion factor.

[0067] Please see Figure 2In this application, areas adjacent to the occurrence of red imported fire ants are marked as predicted areas, including:

[0068] S11. Extract the location of red imported fire ant nests from the database, and mark the area covered by the foraging distance as the radius of the location as the area where red imported fire ants appear. The foraging distance is the foraging radius of the worker ants in the red imported fire ant population.

[0069] S12. Using the location where the red imported fire ant nest appears as the center and the distance between nests as the radius, draw a line to obtain a mapped nest circle; using the due south direction in the mapped nest circle as the starting point and the distance between nests as the distance, set several predicted nests on the mapped nest circle in a counterclockwise rotation; using the predicted nests as the center and the foraging distance as the radius, mark the area covered as the predicted area, where the distance between nests is the preset standard distance between two red imported fire ant nests.

[0070] Please see Figure 3 The locations of red imported fire ant nests are extracted from the database. The area covered by the location with a foraging distance as the radius is marked as the red imported fire ant area Q1. A mapping nest circle Y is obtained by drawing a line with the location of the red imported fire ant nest as the center and the distance between nests as the radius. Starting from the due south direction in the mapping nest circle Y and with the distance between nests as the distance, several predicted nests are set on the mapping nest circle in a counterclockwise rotation. The areas covered by the predicted nests with a foraging distance as the radius are marked as predicted areas Q11, Q12, Q13, Q14, and Q15.

[0071] It is worth noting that this invention marks areas near where red imported fire ants appear as predicted areas. This is a scientific method based on the ecological behavioral characteristics and spatial distribution patterns of red imported fire ants. This process, by combining key parameters such as foraging distance and nest spacing, constructs a dynamic prediction model, which can effectively improve the ability to identify red imported fire ant dispersal paths. Its core objective is to use spatial analysis technology to locate potential red imported fire ant nests in advance, thereby providing accurate data support for prevention and control decisions.

[0072] It should be noted that the foraging distance is determined by human analysis of food resources near the location of red imported fire ant nests, and the foraging distance is inversely proportional to the food resources, such as values ​​of 5 meters, 10 meters, 15 meters, and 20 meters.

[0073] It should be noted that the nest spacing is determined by human analysis of the food resources near the locations of existing red imported fire ant nests, and the nest spacing is inversely proportional to the food resources, such as values ​​of 10 meters, 15 meters, 20 meters, 25 meters, and 30 meters.

[0074] It should be noted that, starting from the due south direction in the mapped nest circle and setting several predicted nests with a distance between nests, the due south direction in my country is generally a region with better temperature and humidity, making it more suitable for the survival of red imported fire ants compared to other directions. Therefore, this invention uses the due south direction as the starting point for planning the predicted nests. Of course, any direction can be used as the starting point; for example, if due to geographical structure or other reasons, the due east direction in the mapped nest circle is more suitable for the survival of red imported fire ants, then the due east direction can be used as the starting point for planning the predicted nests.

[0075] This application obtains environmental data, basic data, and diffusion data for each prediction region, including:

[0076] The system extracts historical temperature, humidity, and annual rainfall data for a predicted area over several days and several years from the weather forecast system. It then calculates the predicted temperature by weighting the maximum, minimum, and average values ​​of the historical temperature, humidity, and annual rainfall data. Finally, it calculates the predicted rainfall by weighting the maximum, minimum, and average values ​​of the historical humidity.

[0077] Soil type, vegetation cover and topography of each prediction area were extracted from the database; the flow rate and start time of the most recent flood in each prediction area were extracted from the database, as well as the start time of several nuptial flight dispersals of red imported fire ants in the history of areas adjacent to each prediction area where red imported fire ants appeared.

[0078] It should be noted that the database includes the Geographic Information System Database, the Remote Sensing Database, the China National Geographic Information Public Service Platform, the Meteorological and Hydrological Database, and the Red Imported Fire Ant Invasion Monitoring Database.

[0079] It should be noted that soil types include sandy soil, loam, clay, and silt; topography includes plains, hills, riverbank wetlands, and urban areas.

[0080] It should be noted that the start times of several nuptial flight dispersals of red imported fire ants throughout history can be determined through monitoring equipment or by summarizing the patterns of their nuptial flight habits. For example, it can be concluded that red imported fire ant populations will conduct nuptial flight dispersals at a certain time of year.

[0081] It should be noted that the weights for the historical maximum, minimum, and average temperatures, historical humidity, and historical annual rainfall are determined manually based on experience. For example, the weight for the maximum historical temperature can be 0.3, the minimum 0.2, and the average 0.5; the weight for the maximum historical humidity can be 0.3, the minimum 0.25, and the average 0.45; and the weight for the maximum historical annual rainfall can be 0.2, the minimum 0.2, and the average 0.6.

[0082] In this application, the survival factors of red imported fire ants in each predicted area are determined based on environmental data, including:

[0083] Temperature WD of each prediction region is extracted sequentially. i Humidity SD i and rainfall YL i , where i is the number of the predicted region;

[0084] When temperature WD is present i Temperatures exceeding the survival range of red imported fire ants, or humidity exceeding the limit. i The humidity level is outside the survival range of red imported fire ants, or there is rainfall. i When the rainfall exceeds the range where red imported fire ants can survive, the current predicted area will be marked as an area where red imported fire ants do not exist;

[0085] When there is no temperature WD i Exceeding the survival temperature range and lacking humidity SD i Exceeding the survival humidity range and with no rainfall YL i When rainfall exceeds the survival range, based on temperature WD i Humidity SD i and rainfall YL i Determine the survival factor C1 of red imported fire ants in the current prediction area. i Survival factor C1 i As shown in the following formula:

[0086]

[0087] In the formula, ZWD is the median value of the survival temperature range, ZSD is the median value of the survival humidity range, and ZYL is the median value of the survival rainfall range; α1, α2 and α3 are all artificially set proportional adjustment coefficients, and α1+α2+α3=1, α1>α2>α3.

[0088] In this application, the range of temperatures, humidity, and rainfall required for the survival of red imported fire ants were obtained through experiments.

[0089] It should be noted that the temperature WD in this application i Humidity SD i Rainfall YL i If any one of the three exceeds the range, the current predicted area will be marked as a region where red imported fire ants do not exist; when the temperature WD i Humidity SD i Rainfall YL i Only when all three are within the specified range can it be concluded that the current environment is suitable for the survival of red imported fire ants. At this point, an assessment of environmental suitability is conducted, which is the first survival factor for red imported fire ants.

[0090] It is worth noting that calculating the survival factors of red imported fire ants in each predicted region is a crucial step in this invention, quantifying the ecological adaptability of red imported fire ants and environmental variables. Its core value lies in systematically assessing the survival potential of red imported fire ants in different regions through scientific modeling and data-driven methods. This process not only deepens our understanding of the distribution patterns of red imported fire ants but also provides actionable decision-making support for invasion control, ecological risk prediction, and resource management.

[0091] It is worth noting that by introducing cross-analysis of multi-dimensional environmental data such as temperature, humidity and rainfall, and combining it with survival thresholds determined by experiments, this invention can more accurately characterize the ecological needs of red imported fire ants, avoiding the one-sidedness caused by relying on traditional single indicators.

[0092] It should be noted that the survival temperature range, survival humidity range, and survival rainfall range were obtained by controlling temperature, humidity, and rainfall in laboratory experiments to test the survival rate of red imported fire ants.

[0093] The survival temperature range is [5℃, 40℃]: When the temperature is below 5℃, the larvae and pupae of red imported fire ants will die, the queen's egg-laying rate will drop sharply, and the population will be difficult to maintain; when the temperature is above 40℃, the moisture in the nest will evaporate rapidly, the worker ants will die from dehydration, and the queen's egg-laying will be hindered.

[0094] The survival humidity range is [40%, 85%]: when the humidity is below 40%, the red imported fire ant larvae will have a high mortality rate due to dry soil; when the humidity is above 85%, the red imported fire ant population will collapse due to fungal infection.

[0095] Rainfall range [500mm, 1500mm]: When the annual rainfall is less than 500mm, red imported fire ants have difficulty surviving due to drought; when the annual rainfall is more than 1500mm, the red imported fire ants experience frequent nest migrations due to frequent rainfall, resulting in unstable populations.

[0096] It should be noted that the proportional adjustment coefficients α1 > α2 > α3 are because: α1 is multiplied by temperature-related data, α2 by humidity-related data, and α3 by rainfall-related data. For red imported fire ants, temperature is the core driving factor of their physiological activities, directly affecting metabolism, reproduction, and survival, and its effect covers the entire population. Therefore, this invention sets the proportional adjustment coefficient for temperature-related data to be the largest. Humidity is crucial for nest structure and larval development, while rainfall mainly affects red imported fire ants indirectly by disrupting nest structure and altering resource distribution. Because the impact on nest structure and larval development is more important than the indirect impact of disrupting nest structure and altering resource distribution, this invention sets the proportional adjustment coefficient for humidity-related data to be greater than that for rainfall-related data.

[0097] In this application, survival factor two for red imported fire ants in each predicted region is determined based on basic data, including:

[0098] Extract soil type, vegetation cover, topography, and survival factor 2 from historical reference data; the historical reference data includes several soil types, vegetation cover, topography, and survival factor 2 set by experts based on soil type, vegetation cover, and topography.

[0099] Soil type, vegetation cover, topography, and survival factor 2 are integrated into several sets of training and testing data. The training data is used to train the artificial intelligence model, and the testing data is used to test the trained artificial intelligence model. The artificial intelligence model is adjusted according to the testing results. Finally, a survival assessment model with soil type, vegetation cover, and topography as input and survival factor 2 as output is obtained. The artificial intelligence model includes a BP neural network model and an RBF neural network model.

[0100] The soil type, vegetation coverage, and topography of each predicted area are sequentially input into the survival assessment model to obtain the second survival factor for each predicted area.

[0101] Specifically, the steps for testing the trained AI model using validation data and adjusting the AI ​​model based on the validation results are as follows:

[0102] The soil type, vegetation cover, and topography from the test data are input into the trained artificial intelligence model to obtain the corresponding survival factor II. The survival factor II is compared with the corresponding survival factor II in the test data. If the difference between the two is within a threshold (obtained empirically), no parameter adjustment is required, and the next set of test data is tested. If it is not within the threshold, the corresponding parameters are adjusted until the difference between the two is within the threshold, and the next set of test data is tested. When the number of test data in which the difference in survival factor II is within the threshold accounts for 90% or more of the total test data, a survival assessment model with soil type, vegetation cover, and topography as input and survival factor II as output is obtained.

[0103] It should be noted that survival factor two represents the survival advantage of red imported fire ants under the influence of soil type, vegetation cover, and topography in the prediction area. The survival factor two used in the training model was obtained by experts through analysis of the corresponding soil type, vegetation cover, topography, and the current population size and density of red imported fire ants in the area.

[0104] The more similar the soil type is to the soil type with a high degree of suitability for the survival of red imported fire ants, the greater the population size and density of red imported fire ants in the region, and the greater the survival factor 2 of the region.

[0105] The closer the vegetation coverage is to the suitable vegetation coverage for red imported fire ants, the greater the population size and density of red imported fire ants in the region, and the greater the survival factor 2 in the region.

[0106] The more the topography and landforms resemble the most suitable topography and landforms for red imported fire ants, the greater the population size and density of red imported fire ants in the region, and the greater the survival factor 2 of the region.

[0107] Among them, the suitable soils for red imported fire ants, ranked from highest to lowest suitability, are: loam, sandy soil, silty soil, and clay soil.

[0108] The suitable vegetation coverage rate is 30% to 60%, and the closer it is to 45%, the greater the survival factor 2 of the area;

[0109] The suitable terrains for red imported fire ants, ranked from highest to lowest suitability, are: plains, urban areas, hilly terrain, and riverside wetlands.

[0110] In this application, survival factor one and survival factor two are integrated into a total survival factor, including:

[0111] Extracting survival factor C1 i And survival factor 2 C2 i Based on survival factor C1 i And survival factor 2 C2 iDetermine the total survival factor ZC i The total survival factor ZC i The calculation formula is as follows:

[0112] ZC i =β1×C1 i +β2×C2 i ;

[0113] In the formula, β1 and β2 are both manually set proportional adjustment coefficients with values ​​of [0,1], and β1+β2=1.

[0114] When the total survival factor ZC of the predicted region i When the value is greater than 1, the total survival factor ZC of the current prediction region is increased. i Set to 1.

[0115] It should be noted that the values ​​of the proportional adjustment coefficients β1 and β2 are related to the location of the region.

[0116] If the region is located in a humid tropical area, the survival factor C2 is calculated based on soil type, vegetation cover, and topography. i Compared to the survival factor C1 calculated from temperature, humidity, and rainfall. i More importantly, in humid tropical regions, temperature, humidity, and rainfall can ensure the survival of red imported fire ants. In this case, it is even more necessary to consider the impact of soil type, vegetation coverage, and topography on the survival of red imported fire ants. Therefore, if the region is located in a humid tropical region, the proportional adjustment coefficient β2 is greater than β1; for example, β1 = 0.4, β2 = 0.6.

[0117] If the region is located in an arid inland area, the survival factor C1 is calculated using temperature, humidity, and rainfall. i The survival factor C2 calculated using soil type, vegetation cover, and topography. i More importantly, in arid inland areas, temperature, humidity and rainfall are key factors limiting the survival of red imported fire ant populations. Therefore, if the area is located in an arid inland region, the proportional regulation coefficient β1 is greater than β2; for example, β1 = 0.7, β2 = 0.3.

[0118] This application determines the flood diffusion factor for each predicted area at the current time based on the flood discharge and start time, including:

[0119] Obtain the flow rate HQ of the most recent flood in each predicted area sequentially. i and start time HT i Determine whether there are areas upstream of the predicted area that have already developed red imported fire ants after the predicted area is affected by flooding from upstream; if yes, determine the distance L between the current predicted area and the upstream area where red imported fire ants have already developed.j , and the flood flow rate HQ i and the start time HT i Determine the flood diffusion factor HZ of the current prediction area at the current time i ; No, set the flood diffusion factor HZ of the current prediction area at the current time i to 0, where the flood diffusion factor HZ i The calculation formula is as follows:

[0120]

[0121] In the formula, j is the number of the distance L between the current prediction area and the area upstream where red imported fire ants appear, and the value range of j is [1, n], and n is the maximum value of the distance L j number; BL is the standard immersion distance set manually, BHQ is the standard flow rate of the flood occurrence set manually, DT is the current time, BC is the standard time length between the time point when red imported fire ants start to reproduce after flood diffusion and the flood occurrence time point; δ1, δ2, and δ3 are all proportion adjustment coefficients with a value range of [0, 1], δ1 + δ2 + δ3 = 1, and δ2 > δ1 > δ3.

[0122] It should be reminded that "judging whether there is an area where red imported fire ants have appeared upstream of the prediction area when the prediction area is invaded by floods generated upstream" is judged according to step S11 in this application, that is, the position of the red imported fire ant nest is extracted from the database, and the area covered by the foraging distance centered on this position is marked as the area where red imported fire ants appear.

[0123] When the flood diffusion factor HZ of the prediction area i is greater than the flood diffusion threshold, the flood diffusion factor HZ of the current prediction area i takes the value of the flood diffusion threshold; among them, the flood diffusion threshold is obtained by empirical setting.

[0124] It should be noted that the standard flow rate BHQ of the flood occurrence is to limit the influence of the flood flow rate on the diffusion effect of red imported fire ants brought by the flood. If the flood is too small, the nest cannot be washed away, resulting in a small diffusion effect of red imported fire ants. If the flood is too large, the survival rate of red imported fire ants is not high, reducing the diffusion effect of red imported fire ants. Only under the condition of appropriate water flow rate can the maximum diffusion effect of red imported fire ants brought by the flood be ensured. Therefore, the present invention uses the formula to quantify the effect of the flood water flow rate on the diffusion of red imported fire ant populations. The formula is a function with a graph line similar to a "person" shape. The flow rate HQ i is closer to the standard flow rate BHQ, the greater the calculation result, and the flow rate HQ​​i The further away from the standard flow rate (BHQ), the smaller the calculated result. This functional relationship can effectively simulate the impact of flood flow on the spread of red imported fire ants, improving the accuracy of the analysis.

[0125] It should be noted that BL is the soaking time of red imported fire ants obtained artificially through laboratory experiments that does not affect the survival rate of red imported fire ants. Then, the standard immersion distance that reflects the survival rate of red imported fire ants is obtained by multiplying the standard flow velocity of the flood obtained by artificial observation by the soaking time.

[0126] It should be noted that BC is a standard time interval artificially set based on the reproductive habits of red imported fire ants, representing the time between the start of their reproduction after flood spread and the time of the flood itself; the current time DT minus the time HT. i The larger the value, the longer the red imported fire ants have to reproduce and rest, further indicating that the probability of red imported fire ants being present in the current area is greater.

[0127] It should be noted that, without considering other conditions, the time HT when the flood occurs in the predicted area is... i The earlier the occurrence of the flood-induced red imported fire ant spread, the longer the recovery period after the spread, and the greater the probability of the presence of red imported fire ant populations in the predicted area.

[0128] It should be noted that the proportional adjustment coefficients δ1, δ2, and δ3 are in the following order: δ2 > δ1 > δ3. This is because the survival rate and nest destruction rate of red imported fire ants directly affect their dispersal and reproduction after a flood. Furthermore, the flood flow rate (HQ) directly influences these factors. i The set proportional adjustment coefficient is greater than the distance L. j and start time HT i The corresponding proportional adjustment coefficients for the relevant data;

[0129] At the same time, the distance from L j This indicates the distance between the current predicted area and the upstream area where red imported fire ants are present. If the distance is L... j If the value is too large, it indicates that it is difficult or impossible for red imported fire ants to spread due to flooding in the current predicted area, and the start time HT... i The relevant data represents the reproduction of red imported fire ants after their spread following the flood; distance L j This indicates whether red imported fire ants will appear in the predicted area, and the start time is HT. i The relevant data indicates the reproduction of red imported fire ants after they appear in the predicted area, therefore, the distance L j The priority for red imported fire ants spreading after flooding is higher than the start time HT i The relevant data, therefore, the distance to Lj The proportional adjustment factor is greater than the start time HT i The proportional adjustment factor for relevant data.

[0130] It should be noted that the flood diffusion threshold is obtained manually through analysis of historical flood-related red imported fire ant diffusion data. It is a threshold reflecting the diffusion and reproduction of red imported fire ants after a flood. When the flood diffusion factor HZ of the predicted area... i When the value is greater than the flood diffusion threshold, it indicates that the current predicted area has met the requirements for post-flood red imported fire ant diffusion. At this point, the flood diffusion factor HZ is adjusted. i Limiting the value of HZ can reduce the higher flood diffusion factor. i The subsequent calculations have a factor CY i impact.

[0131] This application determines the nuptial flight dispersal factor for each predicted region at the current time based on the start time of the red imported fire ant nuptial flight dispersal, including:

[0132] Extract the start time (RT) of several nuptial flight dispersals of red imported fire ants from the historical data of areas adjacent to the predicted regions where red imported fire ants have appeared. r The starting time of the red imported fire ant nuptial flight dispersal RT r If the time difference with the current time is less than the time threshold, then the wedding flight diffusion factor RZ for the current prediction region will be adjusted. i Set to 0; where r is the start time RT of the red imported fire ant nuptial flight dispersal. r The number is given, and the value of r is in the range of [0, z], where z is the start time RT of the red imported fire ant nuptial flight dispersal. r The maximum value of the number;

[0133] The start time of the red imported fire ant nuptial flight dispersal RT r When the time difference with the current time is not less than a time threshold, based on time RT r Determine the diffusion factor RZ for the current prediction region. i The time threshold is determined empirically, and the wedding flight diffusion factor RZ i The calculation formula is as follows:

[0134]

[0135] In the formula, DT is the current time, and DC is the manually set reference duration.

[0136] When the prediction area's wedding flight diffusion factor RZ i When the value is greater than 1, the wedding flight diffusion factor RZ in the current prediction region will be adjusted. i Set to 1.

[0137] It is worth noting that the red imported fire ant, as a highly destructive invasive species, relies heavily on its nuptial flight dispersal behavior as a key link in population expansion and the spread of ecological harm. By using a dynamic calculation method based on historical nuptial flight time data and the current time to determine the nuptial flight dispersal factor for a predicted area, we can not only scientifically quantify the nuptial flight dispersal risk in different regions but also provide a dynamic and precise basis for prevention and control decisions.

[0138] It should be noted that this invention achieves dynamic quantification of the spread risk in the predicted area by extracting the difference between the historical diffusion time of wedding flights in the neighboring area and the current time, and combining it with a time threshold and an exponential function formula.

[0139] It should be noted that the time threshold is set to be shorter than the growth period of a new red imported fire ant population and longer than the time window for red imported fire ant nuptial flights, generally set to 5 days.

[0140] The start time of the red imported fire ant nuptial flight dispersal RT r If the time difference with the current time is less than the time threshold, it means that: in the area adjacent to the current predicted area where red imported fire ants appear, there is only one nuptial flight, and the nuptial flight time is within the time threshold. At this time, the red imported fire ant population in the area adjacent to the current predicted area is either in the process of nuptial flight or has not yet carried out nuptial flight. At this time, there is no new red imported fire ant queen in the current predicted area, indicating that there is no red imported fire ant population in the current predicted area for the time being.

[0141] This application determines the predicted areas where red imported fire ants exist based on the total survival factor, flood diffusion factor, and nuptial flight diffusion factor, including:

[0142] Extract the total survival factor ZC for each prediction region sequentially. i Flood diffusion factor HZ i and the diffusion factor RZ i Based on total survival factor ZC i Flood diffusion factor HZ i and the diffusion factor RZ i Determine the presence factor CY of red imported fire ants in the current prediction area. i The factor CY exists. i The calculation formula is as follows:

[0143] CY i =μ1×RZ i +μ2×ZC i +μ3×HZ i

[0144] In the formula, μ1, μ2 and μ3 are all manually set proportional adjustment coefficients with a value range of [0,1], and μ1+μ2+μ3=1.

[0145] If the presence factor CY of red imported fire ants in the current predicted areai If the value exceeds the existence threshold, the current predicted area is marked as an area where red imported fire ants exist; the existence threshold is determined empirically.

[0146] It should be noted that the values ​​of the proportional adjustment coefficients μ1, μ2, and μ3 are related to the water resources of the region.

[0147] In areas with abundant water resources, flood diffusion is the dominant factor, followed by survival conditions, and the impact on mating flights is minimal. Therefore, the proportional adjustment coefficients are set as μ1 < μ2 < μ3; for example, μ1 = 0.2, μ2 = 0.3, μ3 = 0.5.

[0148] In areas with suitable water resources, the diffusion of the mate flight is dominant, followed by the survival conditions, and the impact of flood diffusion is minimal. Therefore, the proportional adjustment coefficients are set as μ1>μ2>μ3; for example, μ1=0.6, μ2=0.25, μ3=0.15;

[0149] In areas with scarce water resources, survival conditions are the most critical, followed by the spread of offspring, and the impact of floods is the least. Therefore, the proportional adjustment coefficients are set as μ2>μ1>μ3; for example, μ1=0.4, μ2=0.5, μ3=0.1.

[0150] It should be noted that if the presence factor CY of red imported fire ants in the current prediction area is... i If the value is not greater than the existing threshold, the current prediction region is not marked.

[0151] Please see Figure 4 The second aspect of the present invention provides an artificial intelligence-based red imported fire ant habitat prediction system, comprising: a survival identification module, and a data collection module, a region prediction module, and a database connected to the survival identification module;

[0152] Data collection module: Marks areas near where red imported fire ants appear as prediction areas, and acquires environmental data, basic data and diffusion data for each prediction area; among which, environmental data includes temperature, humidity and rainfall; basic data includes soil type, vegetation coverage and topography; diffusion data includes flood flow and start time and the start time of red imported fire ant nuptial flight diffusion;

[0153] Survival identification module: Based on environmental data, determine the first survival factor of red imported fire ants in each prediction area; based on basic data, determine the second survival factor of red imported fire ants in each prediction area; integrate survival factor one and survival factor two into the total survival factor.

[0154] Regional Prediction Module: Determines the flood diffusion factor for each prediction region at the current time based on the flood flow and start time, determines the nuptial flight diffusion factor for each prediction region at the current time based on the start time of the red imported fire ant nuptial flight diffusion, and determines the prediction region where red imported fire ants exist based on the total survival factor, flood diffusion factor, and nuptial flight diffusion factor.

[0155] Database: Used to store data.

[0156] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0157] Working principle of the invention:

[0158] This invention first marks areas near where red imported fire ants appear as predicted areas. This step uses spatial analysis technology to pinpoint potential red imported fire ant nest locations in advance, thus providing precise data support for prevention and control decisions. Next, it acquires environmental, basic, and diffusion data for each predicted area, providing data support for subsequent analysis. Based on the environmental data, it determines the first survival factor for red imported fire ants in each predicted area. This step uses scientific modeling and data-driven methods to systematically assess the survival potential of red imported fire ants in different areas. Based on the basic data, it determines the second survival factor for red imported fire ants in each predicted area. The first and second survival factors are then integrated into a total survival factor. Based on the flood flow and start time, it determines the flood diffusion factor for each predicted area at the current time. Based on the start time of the red imported fire ant nuptial flight diffusion, it determines the nuptial flight diffusion factor for each predicted area at the current time. This step scientifically quantifies the nuptial flight diffusion risk in different areas, providing a dynamic and precise basis for prevention and control decisions. Finally, based on the total survival factor, flood diffusion factor, and nuptial flight diffusion factor, it determines the predicted areas where red imported fire ants exist.

[0159] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for predicting the habitat of red imported fire ants based on artificial intelligence, characterized in that, include: The areas adjacent to the appearance of red imported fire ants were marked as prediction areas. Environmental data, basic data and diffusion data were obtained for each prediction area. The environmental data included temperature, humidity and rainfall; the basic data included soil type, vegetation cover and topography; and the diffusion data included the flow rate and start time of floods and the start time of red imported fire ant nuptial flight diffusion. Based on environmental data, the survival factor 1 of red imported fire ants in each prediction area was determined. Based on basic data, the survival factor 2 of red imported fire ants in each prediction area was determined. The survival factors 1 and 2 were then integrated into the total survival factor. The flood diffusion factor for each predicted area at the current time is determined based on the flow rate and start time of the flood. The nuptial flight diffusion factor for each predicted area at the current time is determined based on the start time of the red imported fire ant nuptial flight diffusion. The predicted areas where red imported fire ants exist are determined based on the total survival factor, flood diffusion factor, and nuptial flight diffusion factor.

2. The method for predicting the habitat of red imported fire ants based on artificial intelligence according to claim 1, characterized in that, The step of marking areas adjacent to where red imported fire ants appear as predicted areas includes: S11. Extract the location of red imported fire ant nests from the database, and mark the area covered by the foraging distance as the radius of the location as the area where red imported fire ants appear. The foraging distance is the foraging radius of the worker ants in the red imported fire ant population. S12. Using the location where the red imported fire ant nest appears as the center and the distance between nests as the radius, draw a line to obtain a mapped nest circle; using the due south direction in the mapped nest circle as the starting point and the distance between nests as the distance, set several predicted nests on the mapped nest circle in a counterclockwise rotation; using the predicted nests as the center and the foraging distance as the radius, mark the area covered as the predicted area, where the distance between nests is the preset standard distance between two red imported fire ant nests.

3. The method for predicting the habitat of red imported fire ants based on artificial intelligence according to claim 1, characterized in that, The acquisition of environmental data, basic data, and diffusion data for each prediction region includes: The system extracts historical temperature, humidity, and annual rainfall data for a predicted area over several days and several years from the weather forecast system. It then calculates the predicted temperature by weighting the maximum, minimum, and average values ​​of the historical temperature, humidity, and annual rainfall data. Finally, it calculates the predicted rainfall by weighting the maximum, minimum, and average values ​​of the historical humidity. Soil type, vegetation cover and topography of each prediction area were extracted from the database; the flow rate and start time of the most recent flood in each prediction area were extracted from the database, as well as the start time of several nuptial flight dispersals of red imported fire ants in the history of areas adjacent to each prediction area where red imported fire ants appeared.

4. The method for predicting the habitat of red imported fire ants based on artificial intelligence according to claim 1, characterized in that, The survival factor for red imported fire ants in each predicted area, determined based on environmental data, includes: Temperature WD of each prediction region is extracted sequentially. i Humidity SD i and rainfall YL i , where i is the number of the predicted region; When temperature WD is present i Temperatures exceeding the survival range of red imported fire ants, or humidity exceeding the limit. i The humidity level is outside the survival range of red imported fire ants, or there is rainfall. i When the rainfall exceeds the range where red imported fire ants can survive, the current predicted area will be marked as an area where red imported fire ants do not exist; When there is no temperature WD i Exceeding the survival temperature range and without humidity SD i Exceeding the survival humidity range and with no rainfall YL i When the rainfall exceeds the survival range, based on the temperature WD i Humidity SD i and rainfall YL i Determine the survival factor C1 of red imported fire ants in the current prediction area. i The survival factor C1 i Satisfies formula (1): Where ZWD is the median value of the survival temperature range, ZSD is the median value of the survival humidity range, and ZYL is the median value of the survival rainfall range; α1, α2 and α3 are all proportional adjustment coefficients, and α1+α2+α3=1, α1>α2>α3.

5. The method for predicting the habitat of red imported fire ants based on artificial intelligence according to claim 1, characterized in that, The process of determining the second survival factor for red imported fire ants in each prediction area based on basic data specifically involves: inputting the soil type, vegetation cover, and topography of each prediction area into the survival assessment model to obtain the second survival factor for each prediction area. The survival assessment model construction steps include: S21. Extract soil type, vegetation coverage, topography and survival factor 2 from historical reference data. The historical reference data includes several soil types, vegetation coverage, topography and survival factor 2 set by experts based on soil type, vegetation coverage and topography. S22. Integrate soil type, vegetation coverage, topography, and survival factor 2 into several sets of training data and test data; use the training data to train the artificial intelligence model, use the test data to test the trained artificial intelligence model, and adjust the artificial intelligence model according to the test results; finally, obtain a survival assessment model with soil type, vegetation coverage, and topography as input and survival factor 2 as output, where the artificial intelligence model includes a BP neural network model and an RBF neural network model.

6. The method for predicting the habitat of red imported fire ants based on artificial intelligence according to claim 1, characterized in that, The integration of survival factor one and survival factor two into a total survival factor includes: Extracting survival factor C1 i And survival factor 2 C2 i Based on the survival factor C1 i And survival factor 2 C2 i Determine the total survival factor ZC i The total survival factor ZC i Satisfies formula (2): ZC i =β1×C1 i +β2×C2 i (2) Where β1 and β2 are both proportional adjustment coefficients with values ​​of [0,1], and β1+β2=1; When the total survival factor ZC of the predicted region i When the value is greater than 1, the total survival factor ZC of the current prediction region is increased. i Set to 1.

7. The method for predicting the habitat of red imported fire ants based on artificial intelligence according to claim 1, characterized in that, The determination of the flood diffusion factor for each predicted area at the current time based on the flood discharge and start time includes: Obtain the flow rate HQ of the most recent flood in each predicted area sequentially. i and start time HT i Determine whether there are areas upstream of the predicted area that have already developed red imported fire ants after the predicted area is affected by flooding from upstream; if yes, determine the distance L between the current predicted area and the upstream area where red imported fire ants have already developed. j and the flood flow rate HQ i and start time HT i Determine the flood diffusion factor HZ for the current forecast area at the current time. i The flood diffusion factor HZ i If the conditions are met (3), then the flood diffusion factor HZ for the current predicted area in the current time period is used. i Set to 0; Formula (3): Where j is the distance L between the current predicted area and the upstream area where red imported fire ants have appeared. j The number is given, and the value of j is in the range [1, n], where n is the distance L. j The maximum value of the number; BL is the standard soaking distance, BHQ is the standard flow rate of the flood, DT is the current time, BC is the standard duration between the time when red imported fire ants begin to reproduce after the flood spread and the time when the flood occurs; δ1, δ2 and δ3 are all proportional adjustment coefficients with a value range of [0,1], δ1+δ2+δ3=1, and δ2>δ1>δ3; When the flood diffusion factor HZ of the predicted area i When the flood diffusion factor HZ in the current predicted area is greater than the flood diffusion threshold, the flood diffusion factor HZ in the current predicted area will be adjusted. i The value is taken as the flood diffusion threshold.

8. The method for predicting the habitat of red imported fire ants based on artificial intelligence according to claim 1, characterized in that, The determination of the nuptial flight dispersal factor for each predicted region at the current time based on the start time of the red imported fire ant nuptial flight dispersal includes: Extract the start time (RT) of several nuptial flight dispersals of red imported fire ants from the historical data of areas adjacent to the predicted regions where red imported fire ants have appeared. r The starting time of the red imported fire ant nuptial flight dispersal RT r If the time difference with the current time is less than the time threshold, then the wedding flight diffusion factor RZ for the current prediction region will be adjusted. i Set to 0; where r is the start time RT of the red imported fire ant nuptial flight dispersal. r The number is given, and the value of r is in the range of [0, z], where z is the start time RT of the red imported fire ant nuptial flight dispersal. r The maximum value of the number; The start time of the red imported fire ant nuptial flight dispersal RT r When the time difference with the current time is not less than a time threshold, based on time RT r Determine the diffusion factor RZ for the current prediction region. i The wedding flight diffusion factor RZ i Satisfies formula (4): Where DT is the current time and DC is the reference duration; When the prediction area's wedding flight diffusion factor RZ i When the value is greater than 1, the wedding flight diffusion factor RZ in the current prediction region will be adjusted. i Set to 1.

9. The method for predicting the habitat of red imported fire ants based on artificial intelligence according to claim 1, characterized in that, The determination of the predicted area where red imported fire ants exist based on the total survival factor, flood diffusion factor, and nuptial flight diffusion factor includes: Extract the total survival factor ZC for each prediction region sequentially. i Flood diffusion factor HZ i and the diffusion factor RZ i Based on total survival factor ZC i Flood diffusion factor HZ i and the diffusion factor RZ i Determine the presence factor CY of red imported fire ants in the current prediction area. i The existence factor CY i Satisfies formula (5): EN i =μ1×RZ i +μ2×ZC i +μ3×HZ i (5) Where μ1, μ2 and μ3 are all proportional adjustment coefficients with values ​​ranging from [0,1], and μ1+μ2+μ3=1; If the presence factor CY of red imported fire ants in the current predicted area i If the value exceeds the threshold, the current predicted region is marked as a region where red imported fire ants exist.

10. An artificial intelligence-based red imported fire ant habitat prediction system, operating based on the artificial intelligence-based red imported fire ant habitat prediction method according to any one of claims 1-9, characterized in that, include: Survival identification module, and data collection module, regional prediction module and database connected to the survival identification module; Data collection module: Marks areas near where red imported fire ants appear as prediction areas, and acquires environmental data, basic data and diffusion data for each prediction area. The environmental data includes temperature, humidity and rainfall; the basic data includes soil type, vegetation coverage and topography; the diffusion data includes the flow rate and start time of floods and the start time of red imported fire ant nuptial flight diffusion. Survival identification module: Based on environmental data, determine the first survival factor of red imported fire ants in each prediction area; based on basic data, determine the second survival factor of red imported fire ants in each prediction area; integrate survival factor one and survival factor two into the total survival factor. Regional Prediction Module: Determines the flood diffusion factor for each prediction region at the current time based on the flood flow and start time, determines the nuptial flight diffusion factor for each prediction region at the current time based on the start time of the red imported fire ant nuptial flight diffusion, and determines the prediction region where red imported fire ants exist based on the total survival factor, flood diffusion factor, and nuptial flight diffusion factor. Database: Used to store data.

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