Unmanned aerial vehicle pest monitoring method based on intelligent path planning

Abstract

The invention relates to the field of unmanned aerial vehicle path adjustment, and discloses an unmanned aerial vehicle pest monitoring method based on intelligent path planning, which is used for solving the problem that the pest condition cannot be monitored in time when the pest distribution is dynamically changed, and comprises the following steps: averagely dividing a region needing pest monitoring into n regions which are recorded as monitoring sub-regions, detecting meteorological data of each monitoring sub-region, calculating to obtain a meteorological influence coefficient, obtaining illumination condition data of each monitoring sub-region, calculating to obtain an illumination influence coefficient, obtaining historical insect disaster data of each monitoring sub-region, evaluating to obtain a historical disaster coefficient, and comprehensively evaluating to obtain a disaster index. The monitoring sub-areas are divided in real time according to the disaster indexes, path planning is performed in real time according to the divided areas by using the algorithm A, and the unmanned aerial vehicle performs pest monitoring according to the real-time path planning, so that the effectiveness of unmanned aerial vehicle path planning is effectively improved, and the damage rate of crops is reduced.

Description

Technical Field

[0001] The present invention relates to the field of drone path adjustment, and more specifically to a drone pest monitoring method based on intelligent path planning. Background Art

[0002] The drone's intelligent path planning uses algorithms and real-time data analysis to dynamically optimize flight paths based on the environment, mission requirements, and flight constraints to efficiently cover the target area. It automatically plans and adjusts flight routes based on pest distribution data, meteorological information, and terrain characteristics, ensuring maximum monitoring accuracy and efficiency while minimizing energy consumption and flight time.

[0003] Most existing drone pest monitoring methods identify key areas with high pest incidence based on historical data or prior knowledge. Drones then focus on covering these key areas to save unnecessary flight time and energy. However, the distribution of pests is usually not fixed and may change dynamically. If drones only focus on previously identified key areas, they may not be able to monitor pests in a timely manner, resulting in crop damage.

[0004] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a drone pest monitoring method based on intelligent path planning to solve the problems existing in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for monitoring pests using a drone based on intelligent path planning, comprising the following steps: Step 1: Divide the area to be monitored for pests into n areas, which are recorded as monitoring sub-areas; Step 2: Detect meteorological data of each monitoring sub-area, wherein the meteorological data includes wind speed, wind direction, temperature and humidity data, and obtain a meteorological impact coefficient based on the meteorological data; Step 3: Obtain the lighting condition data of each monitoring sub-area, wherein the lighting condition data includes the sunshine duration and the temperature difference between day and night, and calculate the lighting impact coefficient based on the lighting condition data; Step 4: Obtain historical insect damage data for each monitoring sub-area, including the frequency, duration, and severity of insect damage, and obtain a historical damage coefficient based on the historical insect damage data. Step 5: Calculate the disaster index based on the meteorological impact coefficient, light impact coefficient and historical disaster coefficient. The calculation formula is: , where DT is the disaster index, MI is the meteorological impact coefficient, LE is the sunlight impact coefficient, HD is the historical disaster coefficient, a1, a2, and a3 are the weight coefficients of the meteorological impact coefficient, the sunlight impact coefficient, and the historical disaster coefficient; Step 6: Divide the monitoring sub-areas in real time according to the disaster index; Step 7: Use algorithm A to perform real-time path planning based on the divided areas, and enable the drone to perform pest monitoring based on the real-time path planning.

[0007] Preferably, the step of obtaining the meteorological influence coefficient according to meteorological data is: Obtain the wind speed and direction of each monitoring sub-area and calculate the wind impact; The temperature and humidity data of each monitoring sub-area are standardized, and the temperature and humidity impact degree is obtained based on the standardized temperature and humidity data; The method for obtaining the meteorological influence coefficient based on the wind influence and temperature and humidity influence is as follows: , where MI represents the meteorological influence coefficient, WF represents the wind force influence, and TH represents the temperature and humidity influence.

[0008] Preferably, the step of obtaining the wind speed and wind direction of each monitoring sub-area and calculating the wind influence degree is: Collect wind speed data of the monitoring sub-area in real time, select all monitoring sub-areas adjacent to the monitoring sub-area, record them as adjacent sub-areas, and collect wind speed data of the adjacent sub-areas in real time; Collect wind direction data of the monitoring sub-area and adjacent sub-areas, and convert the wind direction into radians; The method for calculating the vector components of wind in the monitoring sub-area is: , ,in is the component of the horizontal vector of the monitoring sub-area, is the component of the vertical vector of the monitoring sub-region, Expressed in radians; the method for calculating the vector components of the wind for adjacent sub-areas is , ,in is the component of the horizontal vector of the i-th adjacent sub-region, is the component of the vertical vector of the ith adjacent sub-region, It is expressed as the arc of the wind direction of the i-th adjacent sub-area; The wind speed vectors on the horizontal components of the monitoring sub-area and the adjacent sub-area are averaged to obtain the horizontal horizontal component, and the calculation formula is: ,in Expressed as the average level component, is the component of the horizontal vector of the monitoring sub-area, is the component of the horizontal vector of the ith adjacent sub-region, m represents the number of adjacent sub-regions, and the wind speed vectors of the vertical components of the monitored sub-region and the adjacent sub-region are averaged to obtain the average vertical component, which is calculated as follows: , Expressed as the mean vertical component, is the component of the vertical vector of the monitoring sub-region, is the component of the vertical vector of the i-th adjacent sub-region; The method for obtaining the wind influence degree based on the horizontal horizontal component and the average vertical component is: , where WF represents the wind force influence, Expressed as the average level component, Expressed as the mean vertical component.

[0009] Preferably, the step of calculating the illumination influence coefficient according to the illumination condition data is: The average sunshine duration of the monitoring sub-area within the monitoring period is calculated by averaging the sunshine duration to obtain the average sunshine duration; The average day-night temperature difference of the monitoring sub-area during the monitoring period is calculated by averaging the day-night temperature difference; The light impact coefficient is calculated based on the average sunshine duration and the average day and night temperature difference, and its calculation formula is: , where LE is the light impact coefficient, S is the average sunshine duration, T d is the average temperature difference between day and night.

[0010] Preferably, the step of obtaining the historical disaster coefficient based on historical insect disaster data is: Obtain the number of pest occurrences within the detection period for each monitoring sub-area; obtain the duration of each pest occurrence within the detection period; Obtain the loss data of each pest and calculate the degree of damage based on the loss data; The historical disaster coefficient is calculated based on the number of pest occurrences, duration, and degree of damage. The calculation formula is: , where HD represents the historical disaster coefficient, j represents the number of pest occurrences, and sj i is the duration of the i-th pest, ph i is the degree of damage caused by the i-th pest.

[0011] Preferably, the steps of obtaining the loss data of each pest and calculating the degree of damage based on the loss data are: The method for collecting the crop yield reduction rate and affected area of the monitoring sub-area after each pest infestation and obtaining the damage degree based on the crop yield reduction rate and affected area is as follows: , where ph is the degree of damage, sa is the affected area, and jc is the crop yield reduction rate.

[0012] Preferably, the step of dividing the monitoring sub-areas in real time according to the disaster index is: The disaster index is compared with the preset threshold. If the disaster index is less than the preset threshold, it is determined that the probability of insect plague in the area is low, and the area is recorded as a secondary area. If the disaster index is greater than the preset threshold, it is determined that the probability of insect plague in the area is high, and the area is recorded as a key area.

[0013] Preferably, the step of performing real-time path planning based on the acquired key areas is: By adding the "priority weight" parameter to the A algorithm, an optimized A algorithm is obtained, which sets a low path cost for key areas so that the drone will fly to these areas first; at the same time, a high path cost is set for secondary areas to reduce the flight frequency; Using the optimized A algorithm, monitoring routes are generated based on key areas and secondary areas; And obtain new data in real time during the flight, re-divide the area, and update the monitoring route.

[0014] Technical effects and advantages of the present invention: The area requiring pest monitoring is evenly divided into n areas, denoted as monitoring sub-areas. The meteorological data of each monitoring sub-area is detected, and the meteorological impact coefficient is calculated. The lighting condition data of each monitoring sub-area is obtained, and the lighting impact coefficient is calculated. The historical pest disaster data of each monitoring sub-area is obtained, and the historical disaster coefficient is evaluated. A disaster index is obtained through comprehensive evaluation. The monitoring sub-areas are divided in real time according to the disaster index. The A algorithm is used to perform real-time path planning according to the divided areas, and the drone performs pest monitoring according to the real-time path planning, which effectively improves the effectiveness of the drone route planning and reduces the damage rate of crops. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is the overall flow chart of the present invention. DETAILED DESCRIPTION

[0016] The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. In addition, the forms of the various structures described in the following embodiments are merely examples. The drone pest monitoring method based on intelligent path planning involved in the present invention is not limited to the various structures described in the following embodiments. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0017] The present invention provides a method for monitoring pests using an unmanned aerial vehicle (UAV) based on intelligent path planning, comprising the following steps: Step 1: Divide the area to be monitored for pests into n areas, which are recorded as monitoring sub-areas; By dividing a large area into smaller sub-areas, drones can monitor each area individually, avoiding the long flight times required to cover the entire area all at once, thereby improving monitoring speed and efficiency. The divided sub-areas facilitate detailed monitoring and data collection and analysis, allowing for the identification of pest hotspots or changing trends within specific sub-areas, contributing to more accurate identification and location of pest distribution. Sub-area division allows for frequent monitoring in key areas and regular monitoring in secondary areas, achieving tiered monitoring. Furthermore, divided areas make path planning and real-time adjustments more flexible, allowing for more effective optimization of drone flight paths and energy consumption. Zoning monitoring can establish historical data for each sub-area, facilitating comparative analysis of long-term pest trends and providing a scientific basis for future pest prediction and prevention.

[0018] Step 2: Detect meteorological data of each monitoring sub-area, wherein the meteorological data includes wind speed, wind direction, temperature and humidity data, and obtain a meteorological impact coefficient based on the meteorological data; In this embodiment, it should be specifically explained that the step of obtaining the meteorological influence coefficient according to meteorological data is as follows: Obtain the wind speed and direction of each monitoring sub-area and calculate the wind impact; The temperature and humidity data of each monitoring sub-area are standardized, and the method for obtaining the temperature and humidity influence degree based on the standardized temperature and humidity data is as follows: , where TH represents the temperature and humidity impact, t represents the temperature data of the monitored sub-area, and h represents the humidity data of the monitored sub-area. Many pests grow and reproduce most rapidly within a suitable temperature range. Higher temperatures often accelerate their life cycle, leading to rapid population increases. High humidity environments are conducive to the survival of many pests. High humidity can also affect plant health, making them more susceptible to pests, as plants can become more fragile under stress. The method for obtaining the meteorological influence coefficient based on the wind influence and temperature and humidity influence is as follows: , where MI represents the meteorological influence coefficient, WF represents the wind force influence, and TH represents the temperature and humidity influence.

[0019] In this embodiment, it should be specifically explained that the steps of obtaining the wind speed and wind direction of each monitoring sub-area and calculating the wind influence degree are as follows: Collect wind speed data of the monitoring sub-area in real time, select all monitoring sub-areas adjacent to the monitoring sub-area, record them as adjacent sub-areas, and collect wind speed data of the adjacent sub-areas in real time; Collect wind direction data of the monitoring sub-area and adjacent sub-areas, and convert the wind direction into radians. The conversion formula is: ,in Expressed in radians, For wind direction; The method for calculating the vector components of wind in the monitoring sub-area is: , ,in is the component of the horizontal vector of the monitoring sub-area, The vertical vector component of the monitoring sub-area; the method for calculating the vector component of the wind in the adjacent sub-area is: , ,in is the component of the horizontal vector of the i-th adjacent sub-region, is the component of the vertical vector of the i-th adjacent sub-region; The wind speed vectors on the horizontal components of the monitoring sub-area and the adjacent sub-area are averaged to obtain the horizontal horizontal component, and the calculation formula is: ,in Expressed as the average level component, is the component of the horizontal vector of the monitoring sub-area, is the component of the horizontal vector of the ith adjacent sub-region, m represents the number of adjacent sub-regions, and the wind speed vectors of the vertical components of the monitored sub-region and the adjacent sub-region are averaged to obtain the average vertical component, which is calculated as follows: , Expressed as the mean vertical component, is the component of the vertical vector of the monitoring sub-region, is the component of the vertical vector of the i-th adjacent sub-region; The method for obtaining the wind influence degree based on the horizontal horizontal component and the average vertical component is: , where WF represents the wind force influence, Expressed as the average level component, Expressed as the mean vertical component.

[0020] Pests are often affected by wind and move between regions. By combining wind speed and direction within each region and adjacent areas, we can more accurately capture the impact of wind on pest spread, leading to more precise predictions of pest distribution within each region. Wind impact factors help identify areas where pests are more likely to spread. Drones can prioritize monitoring high-risk sub-regions, rationally allocating monitoring resources, reducing waste of manpower and material resources and ensuring timely prevention and control in key areas. Wind speed and direction are dynamic, and wind impact factors generated based on this real-time data can help drones adjust their flight paths to cover areas where new pests may be present, enabling dynamic monitoring and rapid response.

[0021] Step 3: Obtain the lighting condition data of each monitoring sub-area, wherein the lighting condition data includes the sunshine duration and the temperature difference between day and night, and calculate the lighting impact coefficient based on the lighting condition data; Daylight duration affects plant photosynthesis and the growth environment, directly impacting pest reproduction and activity. Diurnal temperature fluctuations, in turn, influence pest physiology and behavior, affecting their growth cycles and activity levels. Light conditions directly impact pest activity and reproduction. The light impact coefficient can be used to more accurately predict the likelihood of pest outbreaks in a specific area, enabling proactive control measures. Daylight duration and diurnal temperature fluctuations are common parameters in meteorological data and are readily available through weather stations or weather services. This simplifies data acquisition and makes it suitable for monitoring and analysis across diverse regions.

[0022] In this embodiment, it should be specifically explained that the step of calculating the illumination influence coefficient according to the illumination condition data is as follows: The average sunshine duration of the monitoring sub-area within the monitoring period is calculated by averaging the sunshine duration to obtain the average sunshine duration; The average day-night temperature difference of the monitoring sub-area during the monitoring period is calculated by averaging the day-night temperature difference; The light impact coefficient is calculated based on the average sunshine duration and the average day and night temperature difference, and its calculation formula is: , where LE is the light impact coefficient, S is the average sunshine duration, T d is the average temperature difference between day and night.

[0023] Step 4: Obtain historical insect damage data for each monitoring sub-area, including the frequency, duration, and severity of insect damage, and obtain a historical damage coefficient based on the historical insect damage data. In this embodiment, it should be specifically explained that the steps of evaluating and obtaining the historical disaster coefficient based on historical insect disaster data are as follows: Obtain the number of pest occurrences in each monitoring sub-area within a detection period, which can be one or two years. A high frequency of pest occurrences may indicate that the area is more susceptible to pests. Obtain the duration of each pest occurrence within the detection period. A longer pest duration generally indicates a higher degree of destructiveness. Obtain the loss data of each pest and calculate the degree of damage based on the loss data; The historical disaster coefficient is calculated based on the number of pest occurrences, duration, and degree of damage. The calculation formula is: , where HD represents the historical disaster coefficient, j represents the number of pest occurrences, and sj i is the duration of the i-th pest, ph i is the degree of damage caused by the i-th pest.

[0024] In this embodiment, it should be specifically explained that the steps of obtaining the loss data of each pest and calculating the damage degree based on the loss data are as follows: The method for collecting the crop yield reduction rate and affected area of the monitoring sub-area after each pest infestation and obtaining the damage degree based on the crop yield reduction rate and affected area is as follows: , where ph is the degree of damage, sa is the affected area, and jc is the crop yield reduction rate.

[0025] Crop yield reduction reflects the direct impact of pests, while the affected area indicates the scope of the impact. Combining the two provides a more comprehensive assessment of the extent of pest losses, reflecting both the depth (yield reduction rate) and breadth (affected area) of the infestation. Both yield reduction and affected area are relatively easy-to-obtain basic data; farmers and monitoring personnel can typically measure or estimate them quickly after an infestation occurs. Because yield reduction and affected area are independent of specific crop physical characteristics, this calculation method is adaptable to a wide range of crop types. Whether used for food crops, cash crops, or other vegetation, these two parameters can be used to assess pest impacts, demonstrating their high adaptability.

[0026] Step 5: Calculate the disaster index based on the meteorological impact coefficient, light impact coefficient and historical disaster coefficient. The calculation formula is: , where DT represents the Distress Index (DT) and MI represents the Meteorological Impact Factor (MI). Different meteorological conditions significantly influence and drive insect pest risk. As wind speed increases or wind direction becomes more favorable for pest migration, high temperatures and humidity promote pest reproduction and activity. The Meteorological Impact Factor (MI) increases, leading to a corresponding increase in the Distress Index (DI). This proportional relationship indicates that when adverse meteorological conditions intensify, the risk of insect pest outbreaks increases. The DI can serve as a key parameter, reflecting the degree to which meteorological conditions in a specific area affect insect pest occurrence, providing a scientific basis for insect pest early warning, monitoring strategy adjustments, and targeted prevention and control. LE represents the Light Impact Factor (LE). Increased daylight duration and a wider diurnal temperature range typically stimulate the reproduction, activity, and spread of certain pests. For example, longer daylight hours can accelerate pest metabolism and growth, while a larger diurnal temperature range can affect the timing or frequency of pest activity. In this context, a higher DI increases the Insect Damage Index, indicating an increased insect pest risk in that area. The light impact coefficient, as an indicator for assessing insect pest risk, reflects the driving effect of light factors on the ecological behavior of pests and provides a scientific reference for pest monitoring and prevention. HD represents the historical disaster coefficient. Past insect disasters have a guiding role in the current insect pest risk. The historical disaster coefficient reflects the frequency and severity of insect disasters in the region in different years. A higher historical disaster coefficient means that the region has experienced frequent insect pests and the environmental conditions are suitable for the breeding or spread of pests. Therefore, the insect disaster index will increase accordingly. a1, a2, and a3 represent the weight coefficients of the meteorological influence coefficient, the light influence coefficient, and the historical disaster coefficient. , the specific values of a1, a2, and a3 are determined by professionals according to actual conditions. For example, a1, a2, and a3 can be 0.4, 0.4, and 0.2; Step 6: Divide the monitoring sub-areas in real time according to the disaster index; In this embodiment, it should be specifically explained that the steps of dividing the monitoring sub-areas in real time according to the disaster index are: The disaster index is compared with the preset threshold. If the disaster index is less than the preset threshold, it is determined that the probability of insect plague in the area is low, and the area is recorded as a secondary area. If the disaster index is greater than the preset threshold, it is determined that the probability of insect plague in the area is high, and the area is recorded as a key area.

[0027] By dividing regions into key and secondary areas, limited monitoring and control resources can be allocated more efficiently. Key areas receive more attention and intervention measures, while secondary areas can adopt relatively simple monitoring measures. Using thresholds for the impact index can promptly identify high-risk areas, enabling rapid and targeted implementation of control measures, thereby reducing the spread and losses of pests. By dividing regions through a data-driven approach, precision agricultural management can be achieved, with differentiated management based on actual conditions, effectively improving the safety and profitability of agricultural production. This approach bases decisions on objective data, reducing the influence of subjective judgment, and thus improving the scientific nature and reliability of decisions. This approach can also establish standardized monitoring processes, facilitating evaluation and comparison by agricultural management departments at all levels, thereby improving the efficiency of the entire monitoring system.

[0028] Step 7: Use Algorithm A to perform real-time path planning based on the acquired key areas, and let the drone perform pest monitoring based on the real-time path planning; The A algorithm is a path planning algorithm based on heuristic search, used to find the optimal path from a starting point to a destination on a map. It estimates the sum of the actual cost (the distance from the starting point to that node) and the heuristic cost (the estimated distance from that node to the destination) of each path node, prioritizing nodes with the lowest cost for expansion, thereby quickly finding the shortest or lowest-cost path. The A algorithm is widely used in path planning and navigation, particularly in scenarios with numerous obstacles or where dynamic path updates are required.

[0029] In this embodiment, it should be specifically explained that the steps of performing real-time path planning based on the acquired key areas are: By adding the "priority weight" parameter to the cost function of algorithm A, an optimized algorithm A is obtained, which sets a lower path cost for key areas so that drones can fly to these areas first; at the same time, a higher cost is set for secondary areas to reduce the flight frequency; Using the optimized A algorithm, the route planning prioritizes coverage of key areas and then generates routes to secondary areas to save energy. The monitoring threshold is set so that after the drone completes the monitoring task of the key area, it will move to the secondary area for auxiliary monitoring. If new data on pest-dense areas is obtained during the flight, the area will be re-divided. The algorithm will automatically adjust its priority and path based on the area division results, allowing the drone to dynamically adjust its flight plan. The drone will fly on a path updated in real time, achieving high-frequency monitoring of key areas and low-frequency monitoring of secondary areas, thereby improving monitoring efficiency.

[0030] The distribution of pests is random and fluid, and may change with wind direction, climate or other factors. By acquiring and updating data on dense pest areas in real time, drones can dynamically adapt to the latest pest situation to ensure that high-density pest areas are monitored and controlled in a timely manner; the monitoring priority and flight path are adjusted according to the new data to concentrate drones in the most harmful areas, thereby avoiding spending too much time in low-risk areas. This path optimization can effectively reduce the consumption of batteries, electricity and pesticides, and improve the efficiency of drone missions; dynamically adjusting flight plans enables drones to cover newly added pest areas more promptly, preventing pests from spreading further, thereby improving the overall effectiveness of pest control. Drones can respond quickly at critical times and locations to achieve more effective pest control; dynamic path planning enables drones to automatically adjust priorities and paths, reducing reliance on manual instructions. In this way, drones can autonomously respond to complex pest situations without human supervision, improving the level of intelligence and automation. Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0031] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A drone pest monitoring method based on intelligent path planning, characterized in that: The following steps are involved: Step 1: Divide the area to be monitored for pests into n areas, which are recorded as monitoring sub-areas; Step 2: Detect meteorological data of each monitoring sub-area, wherein the meteorological data includes wind speed, wind direction, temperature and humidity data, and obtain a meteorological impact coefficient based on the meteorological data; Step 3: Obtain the lighting condition data of each monitoring sub-area, wherein the lighting condition data includes the sunshine duration and the temperature difference between day and night, and calculate the lighting impact coefficient based on the lighting condition data; Step 4: Obtain historical insect damage data for each monitoring sub-area, including the frequency, duration, and severity of insect damage, and obtain a historical damage coefficient based on the historical insect damage data. Step 5: Calculate the disaster index based on the meteorological impact coefficient, light impact coefficient and historical disaster coefficient. The calculation formula is: , where DT is the disaster index, MI is the meteorological impact coefficient, LE is the sunlight impact coefficient, HD is the historical disaster coefficient, a1, a2, and a3 are the weight coefficients of the meteorological impact coefficient, the sunlight impact coefficient, and the historical disaster coefficient; Step 6: Divide the monitoring sub-areas in real time according to the disaster index; Step 7: Use algorithm A to perform real-time path planning based on the divided areas, and enable the drone to perform pest monitoring based on the real-time path planning.

2. The method for drone pest monitoring based on intelligent path planning according to claim 1, characterized in that: The steps of obtaining the meteorological influence coefficient according to meteorological data are as follows: Obtain the wind speed and direction of each monitoring sub-area and calculate the wind impact; The temperature and humidity data of each monitoring sub-area are standardized, and the temperature and humidity impact degree is obtained based on the standardized temperature and humidity data; The method for obtaining the meteorological influence coefficient based on the wind influence and temperature and humidity influence is as follows: , where MI represents the meteorological influence coefficient, WF represents the wind force influence, and TH represents the temperature and humidity influence.

3. The method for drone pest monitoring based on intelligent path planning according to claim 2, characterized in that: The steps of obtaining the wind speed and direction of each monitoring sub-area and calculating the wind influence are as follows: Collect wind speed data of the monitoring sub-area in real time, select all monitoring sub-areas adjacent to the monitoring sub-area, record them as adjacent sub-areas, and collect wind speed data of the adjacent sub-areas in real time; Collect wind direction data of the monitoring sub-area and adjacent sub-areas, and convert the wind direction into radians; The method for calculating the vector components of wind in the monitoring sub-area is: , ,in is the component of the horizontal vector of the monitoring sub-area, is the component of the vertical vector of the monitoring sub-region, Expressed in radians; the method for calculating the vector components of the wind for adjacent sub-areas is , ,in is the component of the horizontal vector of the i-th adjacent sub-region, is the component of the vertical vector of the ith adjacent sub-region, It is expressed as the arc of the wind direction of the i-th adjacent sub-area; The wind speed vectors on the horizontal components of the monitoring sub-area and the adjacent sub-area are averaged to obtain the horizontal horizontal component, and the calculation formula is: ,in Expressed as the average level component, is the component of the horizontal vector of the monitoring sub-area, is the component of the horizontal vector of the ith adjacent sub-region, m represents the number of adjacent sub-regions, and the wind speed vectors of the vertical components of the monitored sub-region and the adjacent sub-region are averaged to obtain the average vertical component, which is calculated as follows: , Expressed as the mean vertical component, is the component of the vertical vector of the monitoring sub-region, is the component of the vertical vector of the i-th adjacent sub-region; The method for obtaining the wind influence degree based on the horizontal horizontal component and the average vertical component is: , where WF represents the wind force influence, Expressed as the average level component, Expressed as the mean vertical component.

4. The method for drone pest monitoring based on intelligent path planning according to claim 1, characterized in that: The steps of calculating the illumination influence coefficient according to the illumination condition data are as follows: The average sunshine duration of the monitoring sub-area within the monitoring period is calculated by averaging the sunshine duration to obtain the average sunshine duration; The average day-night temperature difference of the monitoring sub-area during the monitoring period is calculated by averaging the day-night temperature difference; The light impact coefficient is calculated based on the average sunshine duration and the average day and night temperature difference, and its calculation formula is: , where LE is the light impact coefficient, S is the average sunshine duration, T d is the average temperature difference between day and night.

5. The method for drone pest monitoring based on intelligent path planning according to claim 1, characterized in that: The steps for evaluating the historical insect disaster coefficient based on historical insect disaster data are as follows: Obtain the number of pest occurrences within the detection period for each monitoring sub-area; obtain the duration of each pest occurrence within the detection period; Obtain the loss data of each pest and calculate the degree of damage based on the loss data; The historical disaster coefficient is calculated based on the number of pest occurrences, duration, and degree of damage. The calculation formula is: , where HD represents the historical disaster coefficient, j represents the number of pest occurrences, and sj i is the duration of the i-th pest, ph i is the degree of damage caused by the i-th pest.

6. The method for drone pest monitoring based on intelligent path planning according to claim 5, characterized in that: The steps of obtaining the loss data of each pest and calculating the degree of damage based on the loss data are as follows: The method for collecting the crop yield reduction rate and affected area of the monitoring sub-area after each pest infestation and obtaining the damage degree based on the crop yield reduction rate and affected area is as follows: , where ph is the degree of damage, sa is the affected area, and jc is the crop yield reduction rate.

7. The method for drone pest monitoring based on intelligent path planning according to claim 1, characterized in that: The steps of dividing the monitoring sub-areas in real time according to the disaster index are as follows: The disaster index is compared with the preset threshold. If the disaster index is less than the preset threshold, it is determined that the probability of insect plague in the area is low, and the area is recorded as a secondary area. If the disaster index is greater than the preset threshold, it is determined that the probability of insect plague in the area is high, and the area is recorded as a key area.

8. The method for drone pest monitoring based on intelligent path planning according to claim 1, characterized in that: The steps of performing real-time path planning based on the acquired key areas are as follows: By adding the "priority weight" parameter to the A algorithm, an optimized A algorithm is obtained, which sets a low path cost for key areas so that the drone will fly to these areas first; at the same time, a high path cost is set for secondary areas to reduce the flight frequency; Using the optimized A algorithm, monitoring routes are generated based on key areas and secondary areas; And obtain new data in real time during the flight, re-divide the area, and update the monitoring route.

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