A prediction method for forest pests

Abstract

The present invention discloses a method for predicting forest pests, which relates to the forestry field and includes the following steps: step 1, determining the range of invasive plants; step 2, photographing and identifying invasive plants; step 3, photographing the growth status of invasive plants; step 4, predicting the growth of invasive plants; and step 5, correcting the prediction. The prediction method of the present invention can correct the prediction results of the spread and growth of invasive plants according to the growth status of the invasive plants, thereby increasing the accuracy of the prediction results. The prediction method of the present invention can quickly determine the range of invasive plants within the forestry monitoring range, perform regional correction and species identification, 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. At the same time, the predicted spread range is corrected and calibrated using the new influencing factor template to obtain the final prediction result.

Description

Technical Field

[0001] The present 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 threaten biodiversity but can also have long-term negative impacts on forest landscapes. Therefore, monitoring, controlling, and managing invasive plants is crucial for protecting the ecological environment. Predicting the growth of invasive plants is crucial for their prevention and control. Growth prediction can inform the development of scientific prevention and control strategies, such as selecting the optimal timing, prioritizing, and choosing appropriate methods. This allows for more efficient allocation of human, material, and financial resources, improving prevention and control efficiency.

[0003] Existing invasive plant monitoring methods use drone multispectral cameras to acquire high-resolution vegetation images, avoiding the impact of weather conditions on data collection and ensuring data continuity and accuracy. Secondly, support vector machines and the 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 construct a prediction model, accurate predictions of invasive plant growth trends are achieved. ArcGIS software is used to predict the spatial distribution of invasive plant biomass, and ordinary kriging interpolation methods are used to fine-tune the prediction residuals, improving 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 forest pests, comprising the following steps:

[0007] Step 1: Determine the range of invasive plants; obtain satellite remote sensing data of the monitoring area, and determine the range of invasive plants after invasion range analysis to obtain invasion range 1;

[0008] 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 invasive plant species based on image information 1 and modifies invasion range 1 to obtain invasion range 2.

[0009] Step 3: Photographing the growth status of the invasive plants. The internal photographing device mounted on the bottom of the drone photographs the interior of the plants within the invasion range 2, and transmits the second image information to the control center. The control center determines the growth status of the invasive plants based on the first and second image information to obtain the first growth status. While the internal photographing device is photographing, the dispensing device located at the top of the internal photographing device can dispense expansion sheets, and the external expansion device located at the bottom of the internal photographing device can expand outward, both of which can expand the plants outward to create more space for photographing.

[0010] Step 4: Forecasting the growth of invasive plants: The control center uses prediction calculation 1 to obtain a growth and spread prediction range based on the invasive plant species information and invasion range 2, thereby obtaining prediction range 1;

[0011] Step 5: Forecast correction: The control center matches the influencing factor template according to 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 invasive plant spread growth prediction result.

[0012] Preferably, in step 1, when analyzing the invasive range, the remote sensing image is first preprocessed, and a decision tree model is used to extract and classify the remote sensing image features, 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.

[0013] Preferably, in step 5, when predictive calculation 1 is performed, the control center obtains physiological characteristic parameters corresponding to the invasive plant from the cloud, uses the physiological characteristic parameters as prediction parameters, and performs prediction using the neural network model to obtain prediction range 1. In this preferred embodiment, using the neural network model in combination with plant physiological characteristic parameters to predict invasive plant growth has the advantage of high accuracy.

[0014] 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. The growth state of the invasive plant 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. 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 to facilitate the accuracy of the predicted value.

[0015] Preferably, the internal camera device includes a base box with a removable top that connects to the bottom of the drone, a U-shaped cover vertically mounted on the bottom of the base box, a toothed turntable located within the base box and rotatably connected to the bottom of the base box's inner wall, a first micromotor located within the base box, a power gear located at the actuating end of the first micromotor and meshing with the toothed turntable, and a rope-type camera component located at the top of the toothed turntable, with the actuating end extending through the turntable and into the U-shaped cover. In this preferred embodiment, the internal camera device facilitates the acquisition of internal image information of densely growing invasive plants, thereby facilitating analysis of their growth status.

[0016] Preferably, the rope-type camera assembly includes two rollers symmetrically mounted on the toothed turntable, a second micromotor mounted on the toothed turntable and positioned between the two rollers, a driven gear mounted at the ends of the rollers, a drive gear mounted at the actuating end of the second micromotor and meshing with the driven gears, two arc-shaped plates symmetrically mounted at the bottom of the toothed turntable and positioned within the U-shaped cover, a roller positioned between the two arc-shaped plates and connected to the bottom of the toothed turntable via a support rod, a pull rope having 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 via the roller, and a camera mounted on the outer wall of the pull rope. In this preferred embodiment, the rope-type camera assembly enables motion-based photography of the interior of invasive plants, thereby facilitating the acquisition of more image information.

[0017] Preferably, the delivery device includes a plurality of expansion blades sleeved on the outer wall of the U-shaped cover, two mounting boxes symmetrically disposed on the outer wall of the base box, and a clamping member disposed within the mounting boxes for delivering the expansion blades one by one. In this preferred embodiment, the delivery device facilitates the delivery of expansion blades to invasive plants, thereby creating an expanded space for photographing.

[0018] 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 blocks, two guide pillars symmetrically arranged on the side of the limit plate away from the first magnetic blocks and with ends passing through the installation box, and a spring sleeved on the outer wall of the guide pillars;

[0019] A clamping block slidably connected to the inner wall of the installation box and located above the limit plate, a second magnetic block located on the outer wall of the clamping block away from the expansion piece, and a second electromagnetic block located 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.

[0020] Preferably, the expansion device includes a positioning ring located at the bottom of the U-shaped cover, a plurality of expansion rods hinged at one end to the outer wall of the positioning ring and arranged in a circular array, and a clutch component located at the bottom of the U-shaped cover and within the positioning ring. In this preferred embodiment, the expansion device mechanically expands the interior of the invasive plant to facilitate imaging.

[0021] 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 expansion rod and corresponding to the position of the electromagnetic ring. In this preferred embodiment, the clutch component is used to achieve separation or closure of the multiple expansion rods.

[0022] In summary, the present invention mainly has the following beneficial effects:

[0023] The prediction method of the present invention can correct the prediction results of the spread and growth of invasive plants according to the growth status of the invasive plants, thereby increasing the accuracy of the prediction results;

[0024] The prediction method of the present invention can quickly determine the range of invasive plants within the forestry monitoring range when predicting the spread of invasive plants, and can perform regional correction and species identification on the invasive plants within the range. It uses invasive plant image information to determine the growth status of the invasive plants, predicts the spread of the invasive plants through a prediction model, and determines a new influencing factor template based on the plant growth status. At the same time, the predicted spread range is corrected and calibrated using the new influencing factor template to obtain the final prediction result.

[0025] When the prediction method of the present invention is specifically implemented, remote sensing technology is used to obtain the invasive plant area, which has the advantage of a wide monitoring range. The use of a neural network model combined with plant physical characteristic parameters to predict the growth of invasive plants has the advantage of high accuracy. The prediction is corrected by matching the plant growth status with the influencing factor template, so as to facilitate the accuracy of the predicted value.

[0026] When acquiring image information of invasive plants, an internal shooting device is used to easily obtain internal image information of densely growing invasive plants, so as to facilitate analysis of the growth status of the invasive plants. The internal shooting device uses a rope-type shooting component to achieve motion shooting of the interior of the invasive plants, so as to obtain more image information;

[0027] The delivery device facilitates delivery of expansion sheets to invasive plants, so as to create an expansion space for photographing. The delivery device delivers the expansion sheets one by one through a clamping component.

[0028] The expansion device is used to mechanically expand the interior of the invasive plant to facilitate filming. The clutch component in the expansion device is used to separate or close multiple expansion rods. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flow chart of the prediction method of the present invention;

[0030] Figure 2 It is an axonometric diagram of the overall structure of the device of the present invention;

[0031] Figure 3 It is an exploded view of the overall structure of the device of the present invention;

[0032] Figure 4 This is an exploded view of the internal photographing device structure of the present invention;

[0033] Figure 5 This is an exploded view of the delivery device structure of the present invention;

[0034] Figure 6 This is an exploded view of the rope-type shooting component structure of the present invention;

[0035] Figure 7 This is an exploded view of the structure of the external expansion device of the present invention;

[0036] Figure 8 It is a cross-sectional view of the overall structure of the present invention;

[0037] Figure 9 It is the overall system structure framework diagram of the present invention;

[0038] Figure 10 This is a structural framework diagram of the control center system of the present invention.

[0039] Description of the drawings: 10. Internal camera; 11. Base box; 12. U-shaped cover; 13. Toothed turntable; 14. First micromotor; 15. Power gear; 16. Rope-type camera component; 161. Roller; 162. Second micromotor; 163. Driven gear; 164. Driving gear; 165. Arc plate; 166. Roller; 167. Pull rope; 168. Camera; 20. Dispensing 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

[0040] 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 should not be understood as limiting the present invention.

[0041] The following describes an embodiment of the present invention based on its overall structure. Example

[0042] 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 includes the following steps:

[0043] Step 1: Determine the range of invasive plants; obtain satellite remote sensing data of the monitoring area, and determine the range of invasive plants after invasion range analysis to obtain invasion range 1;

[0044] 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 invasive plant species based on image information 1 and modifies invasion range 1 to obtain invasion range 2.

[0045] Step 3: Photographing the growth status of the invasive plant. The internal photographing device 10 mounted on the bottom of the drone photographs the interior of the plant within invasion range 2, and transmits image information 2 to the control center. The control center determines the growth status of the invasive plant based on image information 1 and image information 2 to obtain growth status 1. While the internal photographing device 10 is photographing, the dispensing device 20 located at the top of the internal photographing device 10 can dispense expansion blades 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 plant outward to create more space for photographing.

[0046] Step 4: Forecasting the growth of invasive plants: The control center uses prediction calculation 1 to obtain a growth and spread prediction range based on the invasive plant species information and invasion range 2, thereby obtaining prediction range 1;

[0047] Step 5: Prediction correction: The control center matches the influencing factor template according to 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 invasive plant spread growth prediction result;

[0048] When analyzing the invasion range in step one, 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. The range of the invasive plant is determined based on the extracted features. In step five, when the prediction calculation 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 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 status 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. The breeding stage corresponds to the environmental climate template, the enemy template, and the auxiliary propagator template.

[0049] It should be noted that in this embodiment, 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 a decision tree model to extract and classify remote sensing image features and determines the range of invasive plants based on the extracted features.

[0050] The drone module in the control center triggers the drone to reach the invasive plant range, and takes pictures of the plants within the invasive plant range, 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 invasive plant range.

[0051] The drone module in the control center triggers the drone to photograph the internal features of the plants within the corrected invasive plant range. The internal photography device 10 carried by the bottom of the drone photographs the interior of the plants within the corrected invasive plant range and transmits the second picture information to the control center. The picture analysis module in the control center extracts the features in the first and second picture information and compares them with the plant feature images in the database module to determine the growth status of the invasive plants. While the internal photography device 10 is photographing, the dispensing device 20 located at the top of the internal photography device 10 can dispense the expansion sheet 21, and the external expansion device 30 located at the bottom of the internal photography device 10 can expand outward, both of which can expand the plants outward to create more shooting space.

[0052] The prediction module in the control center obtains information on invasive plant species and the revised range of invasive plants, and obtains the corresponding physiological characteristic parameters of the invasive plants from the cloud. Using the physiological characteristic parameters as prediction parameters, the neural network model is used to predict and determine the initial spread range.

[0053] The prediction module in the control center obtains the growth status information of the invasive plant and uses it 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 the new prediction parameter. The neural network model is used to adjust the initial spread range to determine the final predicted spread range.

[0054] Furthermore, the influencing factor template includes 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; the breeding stage corresponds to the environmental climate template, the enemy template and the auxiliary propagator template.

[0055] Please refer to the attached Figure 3 、 4 As 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 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 provided in the base box 11, a power gear 15 provided at the execution end of the first micro motor 14 and engaged with the toothed turntable 13, and a rope-type shooting component 16 provided 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, the rope-type shooting component 16 includes two symmetrically arranged on the toothed 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 engaged 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 with 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.

[0056] It should be noted that, in this embodiment, 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 on 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 at the same time through the driving gear 164 and the driven gear 163. 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 shoots with the U-shaped groove on the outer wall of the U-shaped cover 12 as the path.

[0057] Please refer to the attached Figure 4 、 5 As shown in , 7, and 8, in another preferred embodiment of the present invention, the delivery device 20 includes 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, 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 provided on the inner wall of the installation box 22 and abutting against the first magnetic block 232, two guide columns 234 symmetrically arranged on the side of the limit plate 231 away from the first magnetic block 232 and with the end portion passing through the installation box 22, and a spring 235 sleeved on the outer wall of the guide column 234; a spring 235 slidably connected to the inner wall of the installation box 22 and located at the bottom The clamping block 236 on the upper part of the limiting plate 231, the second magnetic block 237 provided on the outer wall of the clamping block 236 away from the expansion plate 21, and the 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, the outward expansion device 30 includes a positioning ring 31 provided at the bottom of the U-shaped cover 12, a plurality of outward expansion rods 32 provided at one end hinged to the outer wall of the positioning ring 31 and distributed in a circular array, and a clutch component 33 provided at the bottom of the U-shaped cover 12 and located on the inner ring of the positioning ring 31, the clutch component 33 includes a positioning rod 331 provided 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 provided on the outer wall of the outward expansion rod 32 and corresponding to the position of the electromagnetic ring 332.

[0058] It should be noted that, in this embodiment, the delivery device 20 can deliver the expansion piece 21. When the expansion piece 21 falls under the action of gravity, the plants will be dispersed 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 polarity as the second magnetic block 237. 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 polarity as the first magnetic block 232. 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 de-energized first, and the limit plate 231 is reset under the action of the spring 235. The second electromagnetic block 238 is de-energized again, and the expansion piece 21 falls onto the limit plate 231.

[0059] Furthermore, the expansion device 30 can mechanically expand the plants 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 its hinge point with the positioning ring 31 until the end of the expansion rod 32 abuts the invasive plant. The drone continues to descend, and the expansion rod 32 continues to rotate around its hinge point with 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;

[0060] 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.

[0061] The working principle of the present invention is:

[0062] The electrical components in the present invention are all triggered to operate by the controller on board the drone, which exchanges data with the control center, which is connected to the cloud via the network;

[0063] When implementing the forecast, 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 a decision tree model to extract and classify remote sensing image features and determine the range of invasive plants based on the extracted features.

[0064] The drone module in the control center triggers the drone to reach the invasive plant range, and takes pictures of the plants within the invasive plant range, 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 invasive plant range.

[0065] The drone module in the control center triggers the drone to photograph the internal features of the plants within the corrected invasive plant range. The internal photography device 10 carried by the bottom of the drone photographs the interior of the plants within the corrected invasive plant range and transmits the second picture information to the control center. The picture analysis module in the control center extracts the features in the first and second picture information and compares them with the plant feature images in the database module to determine the growth status of the invasive plants. While the internal photography device 10 is photographing, the dispensing device 20 located at the top of the internal photography device 10 can dispense the expansion sheet 21, and the external expansion device 30 located at the bottom of the internal photography device 10 can expand outward, both of which can expand the plants outward to create more shooting space.

[0066] The prediction module in the control center obtains information on invasive plant species and the revised range of invasive plants, and obtains the corresponding physiological characteristic parameters of the invasive plants from the cloud. Using the physiological characteristic parameters as prediction parameters, the neural network model is used to predict and determine the initial spread range.

[0067] The prediction module in the control center obtains the growth status information of the invasive plant and uses it 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 the new prediction parameter. The neural network model is used to adjust the initial spread range to determine the final predicted spread range.

[0068] The influencing factor templates include the environmental climate template, living space template, nutrient supply template, enemy template, and auxiliary propagator template. The growth status of invasive plants includes the induction stage, growth stage, and reproduction 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. The reproduction stage corresponds to the environmental climate template, enemy template, and auxiliary propagator template.

[0069] The drone flies until the U-shaped cover 12 is inserted into the invasive plant, and then the internal camera device 10 starts filming. When the internal camera device 10 films, 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 on the outer wall of the U-shaped cover 12. At this time, the second micro motor 162 drives the two rollers 161 to rotate simultaneously through the driving gear 164 and the driven gear 163. 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 pictures using the U-shaped groove on the outer wall of the U-shaped cover 12 as a path.

[0070] The delivery device 20 can deliver the expansion piece 21. When the expansion piece 21 falls under the action of gravity, the plants will be dispersed 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 polarity as the second magnetic block 237. 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 polarity as the first magnetic block 232. 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 de-energized first. The limit plate 231 is reset under the action of the spring 235. The second electromagnetic block 238 is de-energized again, and the expansion piece 21 falls onto the limit plate 231.

[0071] The expansion device 30 can mechanically expand the plants to create a shooting space. When the expansion device 30 is in operation, before the drone descends to the limit 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 its hinge point with the positioning ring 31 until the end of the expansion rod 32 abuts the invasive plant. The drone continues to descend, and the expansion rod 32 continues to rotate around its hinge point with 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.

[0072] 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.

[0073] 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 an appropriate 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 embodiment without creative contribution 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 range of invasive plants; obtain satellite remote sensing data of the monitoring area, and determine the range of invasive plants after invasion range analysis to obtain invasion range 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 invasive plant species based on image information 1 and modifies 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 on 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 piece (21), and the external expansion device (30) located at the bottom of the internal photographing device (10) can expand outward, and both can expand the plants outward to create more shooting space; Step 4: Forecasting the growth of invasive plants: The control center uses prediction calculation 1 to obtain a growth and spread prediction range based on the invasive plant species information and invasion range 2, thereby obtaining prediction range 1; Step 5, prediction correction, the control center matches the influencing factor template according to the growth state 1, and obtains the influence data corresponding to the influencing factor in the influencing factor template from the cloud, and uses the influence data to adjust the prediction range 1 to obtain the invasive plant spread growth prediction result, the internal shooting device (10) includes a base box (11) with a top detachable connection 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 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), and a power gear arranged at the execution end of the first micro motor (14) and meshing with the toothed wall turntable (13). A wheel (15), and a rope-type shooting component (16) provided at the top of the toothed turntable (13) and having an execution end passing through the toothed turntable (13) and extending into the U-shaped cover (12), the rope-type shooting component (16) comprising two rollers (161) symmetrically provided on the toothed turntable (13), a second micro motor (162) provided on the toothed 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 circular rollers (161) symmetrically provided at the bottom of the toothed turntable (13) and located in the U-shaped cover (12), and a driving gear (164) provided at the execution end of the second micro motor (162) and meshing with the driven gear (163). An arc plate (165), 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) located on the outer wall of the pull rope (167), the outward expansion device (30) comprising a positioning ring (31) located at the bottom of the U-shaped cover (12), a plurality of outward expansion rods (32) located at one end hinged to the outer wall of the positioning ring (31) and distributed in a circular array, and a clutch component (33) located at the bottom of the U-shaped cover (12) and located on the inner ring of the positioning ring (31), the clutch component (33) 3) comprising a positioning rod (331) provided 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) provided on the outer wall of the expansion rod (32) and corresponding to the position of the electromagnetic ring (332), the delivery device (20) comprising a plurality of expansion pieces (21) sleeved on the outer wall of the U-shaped cover (12), two installation boxes (22) symmetrically provided on the outer wall of the base box (11), and a clamping component (23) provided in the installation box (22) and used for delivering the expansion pieces (21) one by one, the clamping component (23) comprising 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 limiting 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 limiting plate (231) away from the first magnetic block (232) and with their ends passing through the installation box (22), a spring (235) sleeved on the outer wall of the guide pillars (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) arranged on the outer wall of the clamping block (236) away from the expansion piece (21), and a 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).

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

3. The method for predicting forest pests according to claim 1, wherein: In step five, when the prediction calculation is performed, 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 the prediction range one.

4. The method for predicting forest pests according to claim 1, wherein: The influencing factor templates include the environmental climate template, living space template, nutrient supply template, enemy 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 enemy template, and the breeding stage corresponds to the environmental climate template, enemy template and auxiliary propagator template.

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