Forestry pest prediction method

Abstract

The invention discloses a forestry pest prediction method, and relates to the forestry field, and the method comprises the following steps: 1, determining an invasive plant range; step 2, shooting and identifying invasive plants; 3, shooting the growth state of the invasive plant; 4, predicting the growth of invasive plants; and 5, predicting and correcting. According to the prediction method, the prediction result of diffusion growth of the invasive plant can be corrected according to the growth state of the invasive plant, and the accuracy of the prediction result is improved; according to the prediction method, the range of the invasive plant can be rapidly determined in the forestry monitoring range, region correction and type identification are carried out, the growth state of the invasive plant is judged by using the image information of the invasive plant, and the diffusion range of the invasive plant is predicted through the prediction model. And determining a new influence factor template according to the plant growth state, and correcting and calibrating the predicted diffusion range by using the new influence factor template to obtain a final prediction result.

Description

Technical Field

[0001] The invention mainly relates to the technical field of forestry, and in particular to a method for predicting forestry harmful organisms. Background Art

[0002] Invasive plants in forestry not only pose a threat to biodiversity, but may also have a long-term negative impact on the natural landscape of forestry. Therefore, the monitoring, control and management of invasive plants is an important task in protecting the ecological environment. The growth prediction of invasive plants is of great significance for prevention and control. Growth prediction can provide a basis for the formulation of scientific prevention and control strategies, such as choosing the best prevention and control time, determining the priority of prevention and control, and choosing appropriate prevention and control methods, so as to more effectively allocate human, material and financial resources and improve the efficiency of prevention and control.

[0003] The existing invasive plant monitoring method uses unmanned aerial vehicle multispectral cameras to obtain high-resolution vegetation images, avoiding the impact of weather conditions on data collection and ensuring the continuity and accuracy of the data; secondly, the support vector machine and KNN algorithm are used to achieve high-precision identification and classification of invasive plants, improving the accuracy of biomass prediction; by combining multispectral data with field-collected biomass data to build a prediction model, accurate prediction of the growth trend of invasive plants is achieved; ArcGIS software is used to predict the spatial distribution of invasive plant biomass, and the ordinary kriging interpolation method is used to fine-tune the prediction residuals, which improves the regional adaptability and accuracy of the model, thereby providing a scientific basis and decision-making support for invasive plant monitoring and management.

[0004] However, when it is not convenient to predict the growth of invasive plants, the proportion of factors affecting the growth of invasive plants is regulated according to the current growth status of the invasive plants. Summary of the invention

[0005] Based on this, the purpose of the present invention is to provide a method for predicting forest pests to solve the technical problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for predicting forestry pests, comprising the following steps: Step 1: Determine the scope of invasive plants; obtain satellite remote sensing data of the monitoring area, and determine the scope of the invasive plants after invasion scope analysis to obtain invasion scope 1; Step 2: Photograph and identify invasive plants; use a drone to photograph plants within invasion range 1, and transmit image information 1 to a control center. The control center determines the species of the invasive plants based on the image information 1 and corrects the invasion range 1 to obtain invasion range 2. Step 3: photographing the growth status of the invasive plants; using the internal photographing device carried at the bottom of the drone to photograph the inside of the plants within the invasion range 2, and transmitting the picture information 2 to the control center, the control center determines the growth status of the invasive plants according to the picture information 1 and the picture information 2 to obtain the growth status 1. When the internal photographing device is photographing, the delivery device located at the top of the internal photographing device can deliver the expansion piece, and the external expansion device located at the bottom of the internal photographing device can expand outward, and both can expand the plants outward, so as to create more shooting space; Step 4: Growth prediction of invasive plants: the control center obtains the growth and diffusion prediction range by using prediction calculation one according to the invasive plant species information and invasion range two, so as to obtain prediction range one; Step 5: Prediction correction: the control center matches the influencing factor template according to the growth state 1, obtains the impact data corresponding to the influencing factors in the influencing factor template from the cloud, and uses the impact data to adjust the prediction range 1 to obtain the prediction result of the spread growth of invasive plants.

[0007] Preferably, when analyzing the invasion range in step 1, the remote sensing image needs to be preprocessed first, and the remote sensing image features are extracted and classified using a decision tree model, and the invasive plant range is determined based on the extracted features. In this preferred embodiment, obtaining the invasive plant area through remote sensing technology has the advantage of a wide monitoring range.

[0008] Preferably, in step 5, when the prediction calculation 1 is implemented, the control center obtains the physiological characteristic parameters corresponding to the invasive plant from the cloud, uses the physiological characteristic parameters as prediction parameters, and uses the neural network model to perform prediction to obtain prediction range 1. In this preferred embodiment, the use of the neural network model combined with the plant physiological characteristic parameters to predict the growth of invasive plants has the advantage of high accuracy.

[0009] Preferably, the influencing factor template includes an environmental climate template, a living space template, a nutrient supply template, an enemy template, and an auxiliary propagator template, and the invasive plant growth state includes an induction stage, a growth stage, and a breeding stage. The induction stage corresponds to the environmental climate template, the living space template, and the nutrient supply template, the growth stage corresponds to the environmental climate template, the nutrient supply template, and the enemy template, and the breeding stage corresponds to the environmental climate template, the enemy template, and the auxiliary propagator template. In this preferred embodiment, the plant growth state is matched with the influencing factor template to perform a correction prediction so as to make the prediction value more accurate.

[0010] Preferably, the internal shooting device includes a base box with a top that is detachably connected to the bottom of the drone, a U-shaped cover vertically arranged at the bottom of the base box, a toothed wall turntable located in the base box and rotatably connected to the bottom of the inner wall of the base box, a first micro motor arranged in the base box, a power gear arranged at the execution end of the first micro motor and meshing with the toothed wall turntable, and a rope-type shooting component arranged at the top of the toothed wall turntable and the execution end passes through the toothed wall turntable and extends into the U-shaped cover. In this preferred embodiment, the internal shooting device facilitates the acquisition of internal image information of densely growing invasive plants, so as to analyze the growth status of the invasive plants.

[0011] Preferably, the rope-type shooting component includes two rollers symmetrically arranged on the toothed wall rotating disk, a second micro motor arranged on the toothed wall rotating disk and located between the two rollers, a driven gear arranged at the end of the roller, a driving gear arranged at the execution end of the second micro motor and meshing with the driven gear, two arc plates symmetrically arranged at the bottom of the toothed wall rotating disk and located in the U-shaped cover, a roller located between the two arc plates and connected to the bottom of the toothed wall rotating disk through a support rod, a pull rope with one end connected to the outer wall of one of the rollers and the other end connected to the outer wall of the other roller through the roller, and a camera arranged on the outer wall of the pull rope. In this preferred embodiment, the rope-type shooting component is used to realize motion shooting inside the invasive plant, so as to obtain more image information.

[0012] Preferably, the delivery device comprises a plurality of expansion sheets sleeved on the outer wall of the U-shaped cover, two installation boxes symmetrically arranged on the outer wall of the base box, and a clamping component arranged in the installation box and used to deliver the expansion sheets one by one. In this preferred embodiment, the delivery device facilitates delivery of expansion sheets to invasive plants, so as to create an expansion space for shooting.

[0013] Preferably, the clamping component includes a limit plate slidably connected to the bottom of the inner wall of the installation box, two first magnetic blocks symmetrically arranged on one side of the limit plate, a first electromagnetic block arranged on the inner wall of the installation box and abutting against the first magnetic block, two guide pillars symmetrically arranged on the side of the limit plate away from the first magnetic block and with ends passing through the installation box, and a spring sleeved on the outer wall of the guide pillar; A clamping block slidably connected to the inner wall of the installation box and located at the upper part of the limit plate, a second magnetic block arranged on the outer wall of the clamping block away from the expansion piece, and a second electromagnetic block arranged on the inner wall of the installation box and corresponding to the position of the second magnetic block. In this preferred embodiment, the expansion pieces are placed one by one through the clamping component.

[0014] Preferably, the expansion device includes a positioning ring disposed at the bottom of the U-shaped cover, a plurality of expansion rods disposed at one end hinged to the outer wall of the positioning ring and distributed in a circular array, and a clutch component disposed at the bottom of the U-shaped cover and located at the inner ring of the positioning ring. In this preferred embodiment, the expansion device is used to achieve mechanical expansion of the interior of the invasive plant for easy photography.

[0015] Preferably, the clutch component includes a positioning rod disposed at the bottom of the U-shaped cover and coinciding with the center of the positioning ring, an electromagnetic ring sleeved on the outer wall of the positioning rod, and a third magnetic block disposed on the outer wall of the outward expansion rod and corresponding to the position of the electromagnetic ring. In this preferred embodiment, the clutch component is used to achieve separation or closing of multiple outward expansion rods.

[0016] In summary, the present invention mainly has the following beneficial effects: The prediction method of the present invention can correct the prediction result of the spread growth of invasive plants according to the growth state of the invasive plants, thereby increasing the accuracy of the prediction result; The prediction method of the present invention can quickly determine the range of invasive plants within the forestry monitoring range when predicting the spread range of invasive plants, and can perform regional correction and species identification on the invasive plants within the range, use invasive plant image information to determine the growth status of invasive plants, predict the spread range of invasive plants through a prediction model, and determine a new influencing factor template based on the plant growth status, and use the new influencing factor template to correct and calibrate the predicted spread range to obtain the final prediction result; When the prediction method of the present invention is implemented, the invasive plant area obtained by remote sensing technology has the advantage of a wide monitoring range, and the invasive plant growth prediction using a neural network model combined with plant physical characteristic parameters has the advantage of high accuracy. The prediction is corrected by matching the plant growth state with the influencing factor template, so as to make the prediction value more accurate. When obtaining image information of invasive plants, the internal image information of densely growing invasive plants can be obtained through the internal shooting device, so as to analyze the growth status of the invasive plants. The internal shooting device uses a rope-type shooting component to realize motion shooting inside the invasive plants, so as to obtain more image information. The delivery device facilitates delivery of expansion sheets to invasive plants, so as to create expansion space for shooting. The delivery device implements delivery of expansion sheets one by one through a clamping component. The expansion device is used to mechanically expand the interior of the invasive plant to facilitate photographing. The clutch component in the expansion device is used to separate or close multiple expansion rods. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of the prediction method of the present invention; Figure 2 It is an axonometric diagram of the overall structure of the device of the present invention; Figure 3 It is an exploded view of the overall structure of the device of the present invention; Figure 4 It is an exploded view of the internal photographing device structure of the present invention; Figure 5 It is an exploded view of the structure of the delivery device of the present invention; Figure 6 An exploded view of the rope-type shooting component structure of the present invention; Figure 7 It is an exploded view of the structure of the external expansion device of the present invention; Figure 8 It is a cross-sectional view of the overall structure of the present invention; Fig. 9 It is the overall system structure framework diagram of the present invention; Fig.10 This is a structural framework diagram of the control center system of the present invention.

[0018] Description of the drawings: 10, internal shooting device; 11, base box; 12, U-shaped cover; 13, toothed wall turntable; 14, first micro motor; 15, power gear; 16, rope-type shooting component; 161, roller; 162, second micro motor; 163, driven gear; 164, driving gear; 165, arc plate; 166, roller; 167, pull rope; 168, camera; 20, delivery device; 21 , expansion piece; 22, installation box; 23, clamping component; 231, limit plate; 232, first magnetic block; 233, first electromagnetic block; 234, guide column; 235, spring; 236, clamping block; 237, second magnetic block; 238, second electromagnetic block; 30, outward expansion device; 31, positioning ring; 32, outward expansion rod; 33, clutch component; 331, positioning rod; 332, electromagnetic ring; 333, third magnetic block. DETAILED DESCRIPTION

[0019] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0020] The following describes an embodiment of the present invention based on its overall structure. Example

[0021] Please refer to the attached Figure 1 , 2 As shown in Figures 9 and 10, in a preferred embodiment of the present invention, a method for predicting forestry pests comprises the following steps: Step 1: Determine the scope of invasive plants; obtain satellite remote sensing data of the monitoring area, and determine the scope of the invasive plants after invasion scope analysis to obtain invasion scope 1; Step 2: Photograph and identify invasive plants; use a drone to photograph plants within invasion range 1, and transmit image information 1 to a control center. The control center determines the species of the invasive plants based on the image information 1 and corrects the invasion range 1 to obtain invasion range 2. Step 3: photographing the growth status of the invasive plants; using the internal photographing device 10 carried at the bottom of the drone to photograph the inside of the plants within the invasion range 2, and transmitting the picture information 2 to the control center, the control center determines the growth status of the invasive plants according to the picture information 1 and the picture information 2 to obtain the growth status 1. When the internal photographing device 10 is photographing, the delivery device 20 located at the top of the internal photographing device 10 can deliver the expansion sheet 21, and the external expansion device 30 located at the bottom of the internal photographing device 10 can expand outward, both of which can expand the plants outward, so as to create more shooting space; Step 4: Growth prediction of invasive plants: the control center obtains the growth and diffusion prediction range by using prediction calculation one according to the invasive plant species information and invasion range two, so as to obtain prediction range one; Step 5: Prediction correction: the control center matches the influencing factor template according to the growth state 1, obtains the influencing data corresponding to the influencing factors in the influencing factor template from the cloud, and uses the influencing data to adjust the prediction range 1 to obtain the prediction result of the spread growth of invasive plants; In step one, when analyzing the invasion range, the remote sensing image needs to be preprocessed first, and the decision tree model is used to extract and classify the features of the remote sensing image, and the range of the invasive plants is determined according to the extracted features. In step five, when the prediction calculation is implemented, the control center obtains the physiological characteristic parameters corresponding to the invasive plants from the cloud, takes the physiological characteristic parameters as prediction parameters, and uses the neural network model to predict to obtain the prediction range one. The influencing factor templates include the environmental climate template, the living space template, the nutrient supply template, the enemy template and the auxiliary propagator template. The growth state of the invasive plant includes the induction stage, the growth stage and the breeding stage. The induction stage corresponds to the environmental climate template, the living space template and the nutrient supply template, the growth stage corresponds to the environmental climate template, the nutrient supply template and the enemy template, and the breeding stage corresponds to the environmental climate template, the enemy template and the auxiliary propagator template.

[0022] It should be noted that, in this embodiment, when implementing the prediction, the remote sensing data module in the control center obtains the satellite remote sensing data of the monitoring area from the cloud and performs radiation correction and atmospheric correction on the remote sensing data. The image analysis module in the control center uses a decision tree model to extract and classify the features of the remote sensing image, and determines the scope of the invasive plant according to the extracted features. The drone module in the control center triggers the drone to reach the range of invasive plants, and takes pictures of the plants within the range of invasive plants, and transmits the picture information to the control center. The picture analysis module in the control center extracts the picture information features and compares them with the plant feature pictures in the database module to determine the invasive plant species. The range correction module in the control center receives the picture information and the drone BDS positioning information, and corrects the range of invasive plants. The drone module in the control center triggers the drone to shoot the internal features of the plants within the corrected invasive plant range. The internal shooting device 10 carried at the bottom of the drone shoots the inside of the plants within the corrected invasive plant range and transmits the picture information 2 to the control center. The picture analysis module in the control center extracts the features in the picture information 1 and the picture information 2 and compares them with the plant feature pictures in the database module to determine the growth status of the invasive plants. When the internal shooting device 10 shoots, the delivery device 20 located at the top of the internal shooting device 10 can deliver the expansion sheet 21, and the external expansion device 30 located at the bottom of the internal shooting device 10 can expand outward, both of which can expand the plants outward, so as to create more shooting space; The prediction module in the control center obtains the invasive plant species information and the corrected invasive plant range information, and obtains the corresponding physiological characteristic parameters of the invasive plants from the cloud. The physiological characteristic parameters are used as prediction parameters and the neural network model is used for prediction to obtain the initial diffusion range. The prediction module in the control center obtains the growth status information of the invasive plants, and uses the growth status information to match the corresponding influencing factor template. At the same time, it obtains the impact data information corresponding to the influencing factor template from the cloud, and uses the impact data information as a new prediction parameter. The neural network model is used to adjust the initial diffusion range to determine the final predicted diffusion range; Furthermore, the influencing factor templates include environmental climate templates, living space templates, nutrient supply templates, pest templates and auxiliary propagator templates; the growth states of invasive plants include induction stage, growth stage and breeding stage; the induction stage corresponds to the environmental climate template, living space template and nutrient supply template; the growth stage corresponds to the environmental climate template, nutrient supply template and pest template; the breeding stage corresponds to the environmental climate template, pest template and auxiliary propagator template.

[0023] Please refer to the attached Figure 3 , 4As shown in , 6, and 8, in another preferred embodiment of the present invention, the internal shooting device 10 includes a base box 11 whose top is detachably connected to the bottom of the drone, a U-shaped cover 12 vertically arranged at the bottom of the base box 11, a toothed wall turntable 13 located in the base box 11 and whose bottom is rotatably connected to the bottom of the inner wall of the base box 11, a first micro motor 14 arranged in the base box 11, a power gear 15 arranged at the execution end of the first micro motor 14 and meshing with the toothed wall turntable 13, and a rope-type shooting component 16 arranged at the top of the toothed wall turntable 13 and whose execution end passes through the toothed wall turntable 13 and extends into the U-shaped cover 12, wherein the rope-type shooting component 16 includes two symmetrically arranged on the toothed wall turntable 13 A roller 161, a second micro motor 162 provided on the toothed wall turntable 13 and located between the two rollers 161, a driven gear 163 provided at the end of the roller 161, a driving gear 164 provided at the execution end of the second micro motor 162 and meshing with the driven gear 163, two arc plates 165 symmetrically provided at the bottom of the toothed wall turntable 13 and located in the U-shaped cover 12, a roller 166 located between the two arc plates 165 and connected to the bottom of the toothed wall turntable 13 through a support rod, a pull rope 167 having one end connected to the outer wall of one of the rollers 161 and the other end connected to the outer wall of the other roller 161 through the roller 166, and a camera 168 provided on the outer wall of the pull rope 167.

[0024] It should be noted that, in the present embodiment, the UAV flies until the U-shaped cover 12 is inserted into the inside of the invasive plant and then the internal shooting device 10 starts shooting. When the internal shooting device 10 shoots, the execution end of the first micro motor 14 drives the toothed wall turntable 13 to rotate through the power gear 15 until the position of the camera 168 coincides with the U-shaped groove of the outer wall of the U-shaped cover 12. At this time, the execution end of the second micro motor 162 drives the two rollers 161 to rotate through the driving gear 164 and the driven gear 163 at the same time, one of the rollers 161 releases the pull rope 167, and the other roller 161 retracts the pull rope 167, and the pull rope 167 drives the camera 168 to move, and the camera 168 shoots with the U-shaped groove of the outer wall of the U-shaped cover 12 as the path.

[0025] Please refer to the attached Figure 4 , 5As shown in Figures 7 and 8, in another preferred embodiment of the present invention, the delivery device 20 includes a plurality of expansion sheets 21 sleeved on the outer wall of the U-shaped cover 12, two installation boxes 22 symmetrically arranged on the outer wall of the base box 11, and a clamping component 23 arranged in the installation box 22 and used for delivering the expansion sheets 21 one by one, the clamping component 23 includes a limit plate 231 slidably connected to the bottom of the inner wall of the installation box 22, two first magnetic blocks 232 symmetrically arranged on one side of the limit plate 231, a first electromagnetic block 233 arranged on the inner wall of the installation box 22 and abutting against the first magnetic block 232, two guide pillars 234 symmetrically arranged on the side of the limit plate 231 away from the first magnetic block 232 and with the ends passing through the installation box 22, and a spring 235 sleeved on the outer wall of the guide pillar 234; a spring 235 slidably connected to the inner wall of the installation box 22 and located at the bottom of the base box 11; and The clamping block 236 on the upper part of the limiting plate 231, the second magnetic block 237 arranged on the outer wall of the clamping block 236 away from the expansion plate 21, and the second electromagnetic block 238 arranged on the inner wall of the installation box 22 and corresponding to the position of the second magnetic block 237, the outward expansion device 30 includes a positioning ring 31 arranged at the bottom of the U-shaped cover 12, a plurality of outward expansion rods 32 arranged at one end hinged to the outer wall of the positioning ring 31 and distributed in a circular array, and a clutch component 33 arranged at the bottom of the U-shaped cover 12 and located at the inner ring of the positioning ring 31, the clutch component 33 includes a positioning rod 331 arranged at the bottom of the U-shaped cover 12 and coinciding with the center of the positioning ring 31, an electromagnetic ring 332 sleeved on the outer wall of the positioning rod 331, and a third magnetic block 333 arranged on the outer wall of the outward expansion rod 32 and corresponding to the position of the electromagnetic ring 332.

[0026] It should be noted that, in the present embodiment, the delivery device 20 can deliver the expansion piece 21. The expansion piece 21 will scatter the plants in the process of falling under the action of gravity to create a shooting space. When the delivery device 20 is working, the second electromagnetic block 238 is energized to generate a repulsive force of the same sex as the second magnetic block 237, and the repulsive force pushes the clamping block 236. The two clamping blocks 236 cooperate to clamp and fix the expansion pieces 21 except the bottom expansion piece 21. At this time, the first electromagnetic block 233 is energized to generate a repulsive force of the same sex as the first magnetic block 232, and the repulsive force pushes the limit plate 231 to move. The limit plate 231 cancels the limit, and the expansion piece 21 at the bottom automatically falls. After the expansion piece 21 at the bottom is delivered, the first electromagnetic block 233 is powered off first, and the limit plate 231 is reset under the action of the spring 235. The second electromagnetic block 238 is powered off again, and the expansion piece 21 falls onto the limit plate 231. Furthermore, the expansion device 30 can mechanically expand the plant to create a shooting space. When the expansion device 30 is working, before the drone descends to the extreme position, the electromagnetic ring 332 is energized to generate a repulsive force of the same polarity as the third magnetic block 333. Under the action of the repulsive force, the expansion rod 32 rotates around the hinge point between it and the positioning ring 31 until the end of the expansion rod 32 abuts against the invasive plant. The drone continues to descend, and the expansion rod 32 continues to rotate around the hinge point between it and the positioning ring 31. At this time, the expansion rod 32 uses the positioning ring 31 as a fulcrum to push the invasive plant outward. During recovery, the drone rises, and the electromagnetic ring 332 is energized to generate an opposite suction force to the third magnetic block 333 , so that the electromagnetic ring 332 magnetically attracts the third magnetic block 333 to complete the reset.

[0027] The working principle of the present invention is: The electrical components in the present invention are all triggered to run by the controller carried on the drone, the controller exchanges data with the control center, and the control center is connected to the cloud through the network; When implementing the prediction, the remote sensing data module in the control center obtains satellite remote sensing data of the monitoring area from the cloud and performs radiation correction and atmospheric correction on the remote sensing data. The image analysis module in the control center uses the decision tree model to extract and classify the features of the remote sensing images and determines the scope of the invasive plants based on the extracted features. The drone module in the control center triggers the drone to reach the range of invasive plants, and takes pictures of the plants within the range of invasive plants, and transmits the picture information to the control center. The picture analysis module in the control center extracts the picture information features and compares them with the plant feature pictures in the database module to determine the invasive plant species. The range correction module in the control center receives the picture information and the drone BDS positioning information, and corrects the range of invasive plants. The drone module in the control center triggers the drone to shoot the internal features of the plants within the corrected invasive plant range. The internal shooting device 10 carried at the bottom of the drone shoots the inside of the plants within the corrected invasive plant range and transmits the picture information 2 to the control center. The picture analysis module in the control center extracts the features in the picture information 1 and the picture information 2 and compares them with the plant feature pictures in the database module to determine the growth status of the invasive plants. When the internal shooting device 10 shoots, the delivery device 20 located at the top of the internal shooting device 10 can deliver the expansion sheet 21, and the external expansion device 30 located at the bottom of the internal shooting device 10 can expand outward, both of which can expand the plants outward, so as to create more shooting space; The prediction module in the control center obtains the invasive plant species information and the corrected invasive plant range information, and obtains the physiological characteristic parameters corresponding to the invasive plants from the cloud. The physiological characteristic parameters are used as prediction parameters and the neural network model is used for prediction to obtain the initial diffusion range. The prediction module in the control center obtains the growth status information of the invasive plants, and uses the growth status information to match the corresponding influencing factor template. At the same time, it obtains the impact data information corresponding to the influencing factor template from the cloud, and uses the impact data information as a new prediction parameter. The neural network model is used to adjust the initial diffusion range to determine the final predicted diffusion range; The influencing factor templates include the environmental climate template, living space template, nutrient supply template, enemy template and auxiliary propagator template. The growth state of invasive plants includes the induction stage, growth stage and breeding stage. The induction stage corresponds to the environmental climate template, living space template and nutrient supply template, the growth stage corresponds to the environmental climate template, nutrient supply template and enemy template, and the breeding stage corresponds to the environmental climate template, enemy template and auxiliary propagator template. The drone flies until the U-shaped cover 12 is inserted into the invasive plant, and then the internal shooting device 10 starts shooting. When the internal shooting device 10 shoots, the execution end of the first micro motor 14 drives the toothed wall turntable 13 to rotate through the power gear 15 until the position of the camera 168 coincides with the U-shaped groove of the outer wall of the U-shaped cover 12. At this time, the execution end of the second micro motor 162 drives the two rollers 161 to rotate through the driving gear 164 and the driven gear 163 at the same time, one of the rollers 161 releases the pull rope 167, and the other roller 161 retracts the pull rope 167. The pull rope 167 drives the camera 168 to move, and the camera 168 takes the U-shaped groove of the outer wall of the U-shaped cover 12 as a path for shooting; The delivery device 20 can deliver the expansion piece 21. The expansion piece 21 will scatter the plants in the process of falling under the action of gravity to create a shooting space. When the delivery device 20 is working, the second electromagnetic block 238 is energized to generate a repulsive force of the same sex as the second magnetic block 237, and the repulsive force pushes the clamping block 236. The two clamping blocks 236 cooperate to clamp and fix the expansion pieces 21 except the bottom expansion piece 21. At this time, the first electromagnetic block 233 is energized to generate a repulsive force of the same sex as the first magnetic block 232, and the repulsive force pushes the limit plate 231 to move. The limit plate 231 cancels the limit, and the expansion piece 21 at the bottom automatically falls. After the expansion piece 21 at the bottom is delivered, the first electromagnetic block 233 is powered off first, and the limit plate 231 is reset under the action of the spring 235. The second electromagnetic block 238 is powered off again, and the expansion piece 21 falls onto the limit plate 231. The expansion device 30 can mechanically expand the plant to create a shooting space. When the expansion device 30 is working, before the drone descends to the extreme position, the electromagnetic ring 332 is energized to generate a repulsive force of the same polarity as the third magnetic block 333. Under the action of the repulsive force, the expansion rod 32 rotates around the hinge point between it and the positioning ring 31 until the end of the expansion rod 32 abuts against the invasive plant. The drone continues to descend, and the expansion rod 32 continues to rotate around the hinge point between it and the positioning ring 31. At this time, the expansion rod 32 uses the positioning ring 31 as a fulcrum to push the invasive plant outward; During recovery, the drone rises, and the electromagnetic ring 332 is energized to generate an opposite suction force to the third magnetic block 333 , so that the electromagnetic ring 332 magnetically attracts the third magnetic block 333 to complete the reset.

[0028] Although an embodiment of the present invention has been shown and described, this specific embodiment is merely an explanation of the present invention and is not a limitation of the invention. The specific features, structures, materials or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. After reading this specification, those skilled in the art may make modifications, substitutions and variations to the embodiments without creative contributions as needed without departing from the principles and purpose of the present invention. However, as long as they are within the scope of the claims of the present invention, they are protected by patent law.

Claims

1. A method for predicting forest pests, characterized in that: The following steps are involved: Step 1: Determine the scope of invasive plants; obtain satellite remote sensing data of the monitoring area, and determine the scope of the invasive plants after invasion scope analysis to obtain invasion scope 1; Step 2: Photograph and identify invasive plants; use a drone to photograph plants within invasion range 1, and transmit image information 1 to a control center. The control center determines the species of the invasive plants based on the image information 1 and corrects the invasion range 1 to obtain invasion range 2. Step three, photographing the growth status of the invasive plants; using the internal photographing device (10) carried at the bottom of the drone to photograph the interior of the plants within the invasion range 2, and transmitting the picture information 2 to the control center, the control center determines the growth status of the invasive plants based on the picture information 1 and the picture information 2 to obtain the growth status 1. When the internal photographing device (10) is photographing, the delivery device (20) located at the top of the internal photographing device (10) can deliver the expansion sheet (21), and the external expansion device (30) located at the bottom of the internal photographing device (10) can expand outward, both of which can expand the plants outward, so as to create more shooting space; Step 4: Growth prediction of invasive plants: the control center obtains the growth and diffusion prediction range by using prediction calculation one according to the invasive plant species information and invasion range two, so as to obtain prediction range one; Step 5: Prediction correction: the control center matches the influencing factor template according to the growth state 1, obtains the impact data corresponding to the influencing factors in the influencing factor template from the cloud, and uses the impact data to adjust the prediction range 1 to obtain the prediction result of the spread growth of invasive plants.

2. A method for predicting forest pests according to claim 1, characterized in that: When analyzing the invasion range in step one, the remote sensing image needs to be preprocessed first, and the remote sensing image features are extracted and classified using a decision tree model, and the range of invasive plants is determined based on the extracted features.

3. A method for predicting forest pests according to claim 1, characterized in that: In step five, when the prediction calculation one is implemented, the control center obtains the physiological characteristic parameters corresponding to the invasive plants from the cloud, uses the physiological characteristic parameters as prediction parameters, and uses the neural network model to perform prediction to obtain the prediction range one.

4. A method for predicting forest pests according to claim 1, characterized in that: The influencing factor templates include the environmental climate template, living space template, nutrient supply template, pest template and auxiliary propagator template. The growth status of invasive plants includes the induction stage, growth stage and breeding stage. The induction stage corresponds to the environmental climate template, living space template and nutrient supply template, the growth stage corresponds to the environmental climate template, nutrient supply template and pest template, and the breeding stage corresponds to the environmental climate template, pest template and auxiliary propagator template.

5. A method for predicting forest pests according to claim 1, characterized in that: The internal shooting device (10) comprises a base box (11) whose top is detachably connected to the bottom of the drone, a U-shaped cover (12) vertically arranged at the bottom of the base box (11), a toothed turntable (13) located in the base box (11) and whose bottom is rotatably connected to the bottom of the inner wall of the base box (11), a first micro motor (14) arranged in the base box (11), a power gear (15) arranged at the execution end of the first micro motor (14) and meshing with the toothed turntable (13), and a rope-type shooting component (16) arranged at the top of the toothed turntable (13) and whose execution end passes through the toothed turntable (13) and extends into the U-shaped cover (12).

6. A method for predicting forest pests according to claim 5, characterized in that: The rope-type shooting component (16) comprises two rollers (161) symmetrically arranged on the toothed wall rotating disk (13), a second micro motor (162) arranged on the toothed wall rotating disk (13) and located between the two rollers (161), a driven gear (163) arranged at the end of the roller (161), a driving gear (164) arranged at the execution end of the second micro motor (162) and meshing with the driven gear (163), and a second micro motor (162) symmetrically arranged on the toothed wall rotating disk (13) and located between the two rollers (161). Two arc plates (165) located at the bottom of the wall rotating disk (13) and inside the U-shaped cover (12), a roller (166) located between the two arc plates (165) and connected to the bottom of the toothed wall rotating disk (13) via a support rod, a pull rope (167) having one end connected to the outer wall of one of the rotating rollers (161) and the other end connected to the outer wall of the other rotating roller (161) via the roller (166), and a camera (168) arranged on the outer wall of the pull rope (167).

7. A method for predicting forest pests according to claim 5, characterized in that: The delivery device (20) comprises a plurality of expansion pieces (21) sleeved on the outer wall of the U-shaped cover (12), two installation boxes (22) symmetrically arranged on the outer wall of the base box (11), and a clamping component (23) arranged in the installation box (22) and used for delivering the expansion pieces (21) one by one.

8. A method for predicting forest pests according to claim 7, characterized in that: The clamping component (23) comprises a limit plate (231) slidably connected to the bottom of the inner wall of the installation box (22), two first magnetic blocks (232) symmetrically arranged on one side of the limit plate (231), a first electromagnetic block (233) arranged on the inner wall of the installation box (22) and abutting against the first magnetic block (232), two guide pillars (234) symmetrically arranged on a side of the limit plate (231) away from the first magnetic block (232) and with ends passing through the installation box (22), and a spring (235) sleeved on the outer wall of the guide pillar (234); A clamping block (236) slidably connected to the inner wall of the installation box (22) and located on the upper part of the limiting plate (231), a second magnetic block (237) provided on the outer wall of the clamping block (236) away from the expansion plate (21), and a second electromagnetic block (238) provided on the inner wall of the installation box (22) and corresponding to the position of the second magnetic block (237).

9. A method for predicting forest pests according to claim 5, characterized in that: The outward expansion device (30) comprises a positioning ring (31) arranged at the bottom of the U-shaped cover (12), a plurality of outward expansion rods (32) which are hinged at one end to the outer wall of the positioning ring (31) and are distributed in a ring array, and a clutch component (33) which is arranged at the bottom of the U-shaped cover (12) and located inside the positioning ring (31).

10. A method for predicting forest pests according to claim 9, characterized in that: The clutch component (33) comprises a positioning rod (331) disposed at the bottom of the U-shaped cover (12) and coinciding with the center of the positioning ring (31), an electromagnetic ring (332) sleeved on the outer wall of the positioning rod (331), and a third magnetic block (333) disposed on the outer wall of the outward expansion rod (32) and corresponding to the position of the electromagnetic ring (332).

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