Forestry monitoring point data intelligent processing system

Abstract

The invention relates to the technical field of monitoring data processing, in particular to an intelligent processing system for forestry monitoring point data, and the system comprises a monitoring obtaining module which is used for obtaining first monitoring information, second monitoring information, third monitoring information, forest region information and collection dates of forestry pest monitoring points; the first monitoring module is used for analyzing the first hazard parameter according to the first monitoring information; the monitoring data construction module is used for constructing a monitoring grayscale image according to the third monitoring information, the first monitoring information, the forest region information and the acquisition date; the second monitoring module is used for analyzing the second hazard parameter according to the second monitoring information and the third monitoring information; the comprehensive monitoring module is used for analyzing the pest and disease parameter according to the monitoring gray level image and analyzing the pest and disease grade according to the pest and disease parameter; and the grade output module is used for outputting the grade of the plant diseases and insect pests. According to the invention, intelligent monitoring processing of monitoring point data is realized.

Description

Technical Field

[0001] The invention relates to the technical field of monitoring data processing, and in particular to an intelligent processing system for forestry monitoring point data. Background Art

[0002] With the rapid development of the Internet of Things, big data and artificial intelligence technologies, monitoring data has shown explosive growth, which has put forward higher requirements for real-time data processing, accurate analysis and intelligent decision-making. Monitoring data processing technology can achieve rapid processing, effective analysis and intelligent application of massive monitoring data through intelligent, networked and big data means, providing scientific decision-making basis for environmental protection, resource optimization and energy management.

[0003] Chinese Patent Publication No.: CN113111207A discloses a pest control detection information system and its implementation method, including a background server, a user terminal and an insect catching device; the user terminal, the background server and the insect catching device are interconnected; wherein the insect catching device includes a support column, a solar panel, a first fixing rod, a pest trap, a control device and a camera. The present invention uses the above method and structure to obtain the insect photo information sent by the user terminal, the insect catching screen information obtained by the insect catching device and the preset insect information library for matching, so that ordinary pedestrians or tourists can take photos of the pests and upload them to realize the monitoring of the pests in the area. The invention realizes the monitoring of the pests in each area by taking pictures of the captured insects in each area, but does not realize the comprehensive analysis of the data of the collection point and the surrounding environment of the collection point and the data in the monitoring area, and there are problems of low efficiency and inaccurate analysis of the pest data. Summary of the invention

[0004] The object of the present invention is to provide a forestry monitoring point data intelligent processing system to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions: A forestry monitoring point data intelligent processing system, comprising: A monitoring acquisition module is used to obtain the first monitoring information, the second monitoring information, the third monitoring information, the forest area information and the collection date of the forest pest monitoring point; A first monitoring module, used for analyzing the type of the monitored object according to the first monitoring image and the first preset monitoring image, and for analyzing the first hazard parameter according to the first monitoring information and the type of the monitored object; A monitoring data construction module, used to analyze the coordinates of the monitoring points according to the third monitoring information, to analyze the quantity change relationship according to the first monitoring information and the third monitoring information, and to construct a monitoring grayscale image according to the coordinates of the monitoring points, the forest area information, the quantity change relationship and the collection date; A second monitoring module, used for analyzing a second hazard parameter according to the second monitoring information and the third monitoring information; A comprehensive monitoring module, for analyzing pest parameters according to the monitoring grayscale image, for adjusting the analysis process of the pest parameters according to the first hazard parameter and the second hazard parameter, and for analyzing the pest level according to the pest parameters; The level output module is used to output the level of pests and diseases.

[0006] Furthermore, the first monitoring module is provided with a pest analysis unit, which is used to calculate the first hazard parameter according to the type of monitored object and the hazard level of the monitored object through a first hazard parameter analysis formula. The pest analysis unit is provided with a first hazard parameter analysis formula as follows: , Wherein, A(i) represents the first hazard parameter, i represents the monitoring point number, i∈{1,2,3,4,5}i=1, it represents the central monitoring point, and i>1, it represents the non-central monitoring point.

[0007] Further, the monitoring data construction module is provided with a coordinate analysis unit, which is used to analyze the coordinates of the monitoring point according to the third monitoring information: when 0≤θ(i)<90, the coordinate analysis unit analyzes the coordinates of the monitoring point and sets x(i)=S(i)×sinθ(i), y(i)=S(i)×cosθ(i); when 90≤θ(i)<180, the coordinate analysis unit analyzes the coordinates of the monitoring point and sets x(i)=S(i)×sinθ(i), y(i)=S(i)×-cosθ(i); when 180≤θ(i)<270, The coordinate analysis unit analyzes the coordinates of the monitoring point and sets x(i)=S(i)×-sinθ(i), y(i)=S(i)×-cosθ(i); when 270≤θ(i)<360, the coordinate analysis unit analyzes the coordinates of the monitoring point and sets x(i)=S(i)×-sinθ(i), y(i)=S(i)×cosθ(i); wherein θ(i) represents the relative angle, S(i) represents the relative distance, x(i) represents the horizontal coordinate of the monitoring point, y(i) represents the vertical coordinate of the monitoring point, and (x(i), y(i)) represents the trapping coordinate point.

[0008] Furthermore, the monitoring data construction module is further provided with a relationship analysis unit, which is used to calculate the quantity change relationship according to the monitoring target quantity and the third monitoring information through a quantity relationship analysis formula, and the relationship analysis unit is provided with a quantity relationship analysis formula as follows: Q(i)=[N(i)-N(1)] / S(i); Among them, Q(i) represents the quantity change relationship, N(i) represents the number of monitoring targets, and N(1) represents the number of monitoring targets at the central monitoring point.

[0009] Further, the image construction unit uses the coordinate point in the monitoring grayscale image that has the same horizontal coordinate and vertical coordinate as the monitoring point coordinate of the monitoring point as the trapping coordinate point, uses the coordinate point in the monitoring grayscale image that is not the trapping coordinate point as the construction coordinate point, and sets the grayscale value of the trapping coordinate point to L(x(i), y(i)), and sets the grayscale value of the construction coordinate point to L(x, y), where: , , Among them, u represents the grayscale construction parameter, i max It represents the maximum value of the monitoring point number, and its value is equal to the number of monitoring points. (x, y) represents the coordinates of the constructed coordinate point.

[0010] Furthermore, the monitoring data construction module is also provided with a tree analysis unit, which is used to adjust the analysis process of the grayscale value of the construction coordinate point according to the number of trees: when M(k)≥M(k-1), the tree analysis unit determines that the number of trees is stable and does not adjust the analysis process of the grayscale value of the construction coordinate point; when M(k)<M(k-1), the tree analysis unit determines that the number of trees is reduced, and adjusts the analysis process of the grayscale value of the construction coordinate point. The adjusted grayscale value of the construction coordinate point is L1(x,y), and L1(x,y)=L(x,y)×log M(k-1) M(k); where M(k) represents the number of trees in the current analysis period, M(k-1) represents the number of trees in the previous analysis period, k represents the analysis period number, k∈N + ; The monitoring data construction module is also provided with a collection and analysis unit, which is used to optimize the adjustment process of the grayscale value of the construction coordinate point according to the collection date. The grayscale value of the optimized construction coordinate point is L2(x,y), and L2(x,y)=L1(x,y)×T(i)×i max / ∑T(i), where T(i) represents the number of days between the collection date of each monitoring point in the current analysis period and its last collection date.

[0011] Further, the second monitoring module compares the second monitoring image with the preset blade contour, and extracts the blade area according to the comparison result: when Z≥α, the second monitoring module extracts the area corresponding to the preset blade contour in the second monitoring image as the blade area; when Z<α, the second monitoring module does not analyze the blade area; wherein Z represents the similarity between the second monitoring image and the preset blade contour, α represents the similarity threshold, 0.8≤α<1; The second monitoring module calculates the average grayscale value of the leaf area from the grayscale value of the leaf area, which is recorded as R=∑L(X,Y) / NL, where R represents the average grayscale value of the leaf area, L(X,Y) represents the grayscale value of the leaf area, (X,Y) represents the pixel coordinates of the leaf area, and NL represents the number of pixels in the leaf area; The second monitoring module analyzes the second hazard parameter according to the gray value of the leaf area and the average gray value of the leaf area: when L(X,Y)<R×β, the second monitoring module extracts the current analysis pixel as the defective pixel, and counts the number of the defective pixel as the defective number; when L(X,Y)≥R×β, the second monitoring module does not analyze the defective pixel; The second monitoring module calculates a second hazard parameter according to the number of defects and the number of pixels in the leaf area, and sets B=NR / NL; Among them, β represents the defect threshold, B represents the second hazard parameter, and NR represents the number of defects.

[0012] Furthermore, the integrated monitoring module is provided with a pest and disease analysis unit, which is used to analyze pest and disease parameters according to the monitoring grayscale image. The pest and disease analysis unit counts the number of constructed coordinate points satisfying L(x, y)=0 in the monitoring grayscale image as the number of abnormal points, and calculates the pest and disease parameters according to the number of abnormal points through the pest and disease parameter analysis formula. The pest and disease analysis unit is provided with a pest and disease parameter analysis formula as follows: , Where D represents the pest parameter, N1 represents the number of pixels in the monitored grayscale image, N2 represents the number of abnormal points, H(i) represents the tree height, and NH represents the number of trees.

[0013] Furthermore, the integrated monitoring module is further provided with a first hazard analysis unit, which is used to adjust the analysis process of the pest parameter according to the first hazard parameter: when A(i)>P(3) / 2, the first hazard analysis unit determines that the first hazard parameter does not meet the threshold value, and adjusts the analysis process of the pest parameter. The adjusted pest parameter is D1, and D1=D×log A(i)P(3); when A(i)≤P(3) / 2, the first hazard analysis unit determines that the first hazard parameter meets the threshold value and does not adjust the analysis process of the pest and disease parameter; The integrated monitoring module is also provided with a second hazard analysis unit, which is used to optimize the adjustment process of the pest parameter according to the second hazard parameter: when B≥b, the second hazard analysis unit determines that the second hazard parameter does not meet the threshold value, optimizes the adjustment process of the pest parameter, and the optimized pest parameter is D2, setting D2=D1×e B ; When B<b, the second hazard analysis unit determines that the second hazard parameter meets the threshold value and does not optimize the adjustment process of the pest parameter; Where b represents the leaf encroachment threshold.

[0014] Furthermore, the integrated monitoring module is also provided with a level analysis unit for analyzing the level of pests and diseases according to the pest and disease parameters: when D≤d1, the level analysis unit determines that the level of pests and diseases is normal; when d1<D≤d2, the level analysis unit determines that the level of pests and diseases is low risk; when D>d2, the level analysis unit determines that the level of pests and diseases is high risk; wherein d1 represents the first pest and disease level threshold, and d2 represents the second pest and disease level threshold.

[0015] The beneficial effects of the present invention are as follows: the monitoring acquisition module acquires the first monitoring information, the second monitoring information, the third monitoring information, the forest area information and the collection date to improve the accuracy of information acquisition, thereby improving the system's analysis efficiency of the first monitoring information and the accuracy of the first monitoring information analysis; the first monitoring module analyzes the first monitoring information to analyze the first hazard parameter, and uses the first hazard parameter to represent the hazard level relationship of the monitored objects at each monitoring point, thereby improving the system's analysis efficiency of the first monitoring information and the accuracy of the first monitoring information analysis; the monitoring data construction module analyzes the third monitoring information, the first monitoring information, the forest area information and the collection date to construct a monitoring grayscale image, construct the relationship between the information and convert it into an image form, make data change analysis more intuitive, ensure the integrity of the system analysis data, and thus improve the system's analysis of the first monitoring information. The analysis efficiency is improved, and the accuracy of the analysis of the first monitoring information is improved. The second monitoring module analyzes the second monitoring information and the third monitoring information to analyze the second hazard parameter, and the second hazard parameter is used to represent the degree of leaf encroachment by pests in the area near the monitoring point, thereby increasing the diversity of system analysis, thereby improving the system's analysis efficiency for the first monitoring information and the accuracy of the analysis of the first monitoring information. The integrated monitoring module analyzes the monitoring grayscale image, the first hazard parameter and the second hazard parameter to analyze the pest and disease parameters, and the pest and disease parameters are used to represent the pest and disease situation in the forest area, thereby analyzing the pest and disease level, thereby improving the system's analysis efficiency for the first monitoring information and the accuracy of the analysis of the first monitoring information. The level output module outputs the pest and disease level to improve the accuracy of the system output, thereby improving the system's analysis efficiency for the first monitoring information and the accuracy of the analysis of the first monitoring information. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 It is a structural diagram of the intelligent processing system of forestry monitoring point data in this embodiment.

[0018] Figure 2 Schematic diagram of the structure of the first monitoring module in this embodiment.

[0019] Figure 3 This is a schematic diagram of the structure of the monitoring data building module in this embodiment.

[0020] Figure 4Schematic diagram of the structure of the comprehensive monitoring module of this embodiment. DETAILED DESCRIPTION

[0021] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0022] It should be noted that, although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0023] See also Figure 1 As shown, it is a forestry monitoring point data intelligent processing system of this embodiment, including: A monitoring acquisition module is used to obtain first monitoring information, second monitoring information, third monitoring information, forest area information and collection date of a forest pest monitoring point, wherein the monitoring point is a pest trapping point set in a forest area, the first monitoring information includes the number of monitoring targets and a first monitoring image, the second monitoring information refers to the information of trees monitored by the monitoring point, the second monitoring information includes the tree height and the second monitoring image, the third monitoring information refers to the information of the monitoring point, the third monitoring information includes the number of monitoring points, the type of monitoring points and the location of the monitoring point, the first monitoring information is the information of the pests captured by the forest pest monitoring point, the tree height is the average value of the tree height within at least 2 meters of the forest pest monitoring point, the second monitoring image is the image of the tree leaves within 2 meters of the forest pest monitoring point, the forest area information includes the number of trees and the area of ​​the forest area, the forest pest monitoring point is the location where an insect trap is set in the forest area, the monitoring point type includes a central monitoring point and a non-central monitoring point, and the number of central monitoring points should be set to meet Set one, whose position should satisfy the central position in the forest area, the monitoring point position includes a relative angle and a relative distance, the monitoring point position is the relative position of the non-central monitoring point and the central monitoring point, the relative angle is the angle between the non-central monitoring point and the central monitoring point in the north clockwise direction, the relative distance is the distance between the non-central monitoring point and the central monitoring point, the collection date is the acquisition date of the first monitoring information and the second monitoring information, the first monitoring information is obtained by the user collecting the captured pests at the forestry pest monitoring point and then interactively uploading, the second monitoring information is obtained by the infrared sensor and camera installed at the forestry pest monitoring point and uploading, the third monitoring information and forest area information are obtained by user interactive input, the collection date is obtained by system operation data import, in this embodiment, the number of monitoring points is set to 5, including 1 central monitoring point and 4 non-central monitoring points, and the non-central monitoring points are respectively distributed in the northwest, northeast, southwest and southeast directions of the central monitoring point, one in each direction; The first monitoring module is used to analyze the type of the monitored object according to the first monitoring image and the first preset monitoring image, and is also used to analyze the first hazard parameter according to the first monitoring information and the type of the monitored object. The first monitoring module is connected to the monitoring acquisition module.

[0024] See also Figure 2 As shown, the first monitoring module is provided with: The level planning unit is used to plan the hazard level of the monitored objects. The hazard level of the monitored objects is used to indicate the hazard level corresponding to each preset monitored object type, thereby improving the diversity of system analysis. The level planning unit plans the hazard level of the monitored object according to the preset monitored object type, sets the hazard level of the monitored object for trunk pests to g1, sets the hazard level of the monitored object for treetop pests to g2, and sets the hazard level of the monitored object for leaf pests to g3, wherein P(p) represents the hazard level of the monitored object of the preset monitored object type, p represents the preset monitored object type, when p=1, it means that the preset monitored object type is trunk pests, P(1)=g1, when p=2, it means that the preset monitored object type is branch pests, P(2)=g2, when p=3, it means that the preset monitored object type is leaf pests. Pests, P(3)=g3. In this embodiment, the setting of the hazard level of the monitored object is not specifically limited, and those skilled in the art can set it freely. The setting of the hazard level of the monitored object should satisfy g1<g2<g3 and g1+g2≥g3. The optimal values ​​of the hazard level of the monitored object are: g1=2, g2=3, g3=5. The level planning unit analyzes the preset types of monitored objects to analyze the hazard level of the monitored object, and realizes the division of hazard levels of different monitored objects, thereby improving the system's analysis efficiency of the first monitoring information and improving the accuracy of the analysis of the first monitoring information.

[0025] Specifically, in this embodiment, the preset monitoring object types are divided into trunk pests, branch pests, and leaf pests. Trunk pests include longhorn beetles, bark beetles, and wood wasps, etc., branch pests include tortoise shell moths, aphids, and psyllids, etc., and leaf pests include poisonous moths, bamboo locusts, and sawflies, etc.

[0026] Please continue reading Figure 2 As shown, the first monitoring module is provided with: A pest identification unit, used for analyzing the types of monitored objects and realizing the identification and classification of biological species in the monitoring images, the pest identification unit is connected to the grade planning unit; The pest identification unit matches the first monitoring image with the first preset monitoring image, and analyzes the type of the monitored object according to the matching result, wherein: When the first monitoring image successfully matches the first preset monitoring image, the pest identification unit uses the preset monitoring object type corresponding to the successfully matched first preset monitoring image as the monitoring object type; When the first monitoring image fails to match the first preset monitoring image, the pest identification unit does not analyze the type of the monitored object; the pest identification unit analyzes the first monitoring image and the first preset monitoring image to analyze the type of the monitored object, thereby ensuring the accuracy of the first monitoring image analysis by the system, thereby improving the system's analysis efficiency of the first monitoring information and improving the accuracy of the first monitoring information analysis.

[0027] Specifically, the pest identification unit in this embodiment outputs the first monitoring image that fails to match, and the user can identify the type of monitoring object of the first monitoring image that fails to match and upload the first monitoring image identified by the user as the first preset monitoring image to increase the data of the system sample database.

[0028] Please continue reading Figure 2 As shown, the first monitoring module includes: A pest analysis unit, used to analyze the first hazard parameter, and to use the first hazard parameter to represent the hazard level relationship of the monitored object at each monitoring point, so as to achieve a unified analysis of the hazard level of the monitored object, and the pest analysis unit is connected to the pest identification unit; The pest analysis unit calculates the first hazard parameter according to the type of monitored object and the hazard level of the monitored object through a first hazard parameter analysis formula. The pest analysis unit is provided with a first hazard parameter analysis formula as follows: , Wherein, A(i) represents the first hazard parameter, i represents the monitoring point number, i∈{1,2,3,4,5}i=1, represents the central monitoring point, i>1, represents the non-central monitoring point, i=2 represents the non-central monitoring point located northeast of the central monitoring point, i=3 represents the non-central monitoring point located southeast of the central monitoring point, i=4 represents the non-central monitoring point located southwest of the central monitoring point, i=5 represents the non-central monitoring point located northwest of the central monitoring point, F(i,j) represents the type of monitored object, j represents the number of monitored targets, and j represents the number of monitored targets. max Indicates the maximum value of the number of monitoring targets, whose value is equal to the number of monitoring targets; through the analysis of the number of monitoring targets, the hazard level of the monitored objects and the types of monitored objects by the pest analysis unit, the first hazard parameter is analyzed to realize the comprehensive analysis of the monitored objects by the system, thereby improving the analysis efficiency of the system for the first monitoring information and improving the accuracy of the analysis of the first monitoring information.

[0029] Please continue reading Figure 1 As shown, the forestry monitoring point data intelligent processing system also includes: The monitoring data construction module is used to analyze the monitoring point coordinates according to the third monitoring information, to analyze the quantity change relationship according to the first monitoring information and the third monitoring information, and to construct a monitoring grayscale image according to the monitoring point coordinates, forest area information, quantity change relationship and collection date. The monitoring data construction module is connected to the monitoring acquisition module.

[0030] See also Figure 3 As shown, the monitoring data construction module includes: A coordinate analysis unit is used to analyze the coordinates of the monitoring points, and use the coordinates of the monitoring points to represent the positions of the monitoring points in the monitoring grayscale image, so as to realize the analysis of the positions of the monitoring points; The coordinate analysis unit analyzes the coordinates of the monitoring point according to the third monitoring information: when 0≤θ(i)<90, the coordinate analysis unit analyzes the coordinates of the monitoring point and sets x(i)=S(i)×sinθ(i), y(i)=S(i)×cosθ(i); when 90≤θ(i)<180, the coordinate analysis unit analyzes the coordinates of the monitoring point and sets x(i)=S(i)×sinθ(i), y(i)=S(i)×-cosθ(i); when 180≤θ(i)<270, the coordinate analysis unit analyzes the coordinates of the monitoring point and sets x(i)=S(i)×-sinθ(i), y(i)=S(i)× )×-cosθ(i); when 270≤θ(i)<360, the coordinate analysis unit analyzes the coordinates of the monitoring point and sets x(i)=S(i)×-sinθ(i), y(i)=S(i)×cosθ(i); wherein θ(i) represents the relative angle, S(i) represents the relative distance, x(i) represents the horizontal coordinate of the monitoring point, y(i) represents the vertical coordinate of the monitoring point, and (x(i), y(i)) represents the trapping coordinate point; the coordinate analysis unit analyzes the third monitoring information to analyze the coordinates of the monitoring point, thereby ensuring the accuracy of the system analysis data, thereby improving the system analysis efficiency of the first monitoring information and improving the accuracy of the first monitoring information analysis.

[0031] Please continue reading Figure 3 As shown, the monitoring data construction module includes: A relationship analysis unit is used to analyze the quantity change relationship, and use the quantity change relationship to represent the fluctuation relationship between the pest data of each monitoring point and the pest data of the central monitoring point; The relationship analysis unit calculates the quantity change relationship according to the number of monitoring targets and the third monitoring information through a quantity relationship analysis formula. The relationship analysis unit is provided with a quantity relationship analysis formula as follows: Q(i)=[N(i)-N(1)] / S(i); Among them, Q(i) represents the quantity change relationship, N(i) represents the number of monitoring targets, and N(1) represents the number of monitoring targets at the central monitoring point; the relationship analysis unit analyzes the first monitoring information and the third monitoring information to analyze the quantity change relationship, realizes the analysis of the volatility of the monitoring data, increases the diversity of system analysis, thereby improving the system's analysis efficiency of the first monitoring information and improving the accuracy of the analysis of the first monitoring information.

[0032] Please continue reading Figure 3 As shown, the monitoring data construction module includes: An image construction unit, used for constructing a monitoring grayscale image to graphically represent the relationship between the analysis data, the image construction unit being connected to the relationship analysis unit; The image construction unit constructs a monitoring grayscale image according to the monitoring point coordinates, forest area information and quantity change relationship. The number of pixels in the monitoring grayscale image is the same as the size of the forest area. The center point position of the monitoring grayscale image is set as the position of the central monitoring point. A plane rectangular coordinate system is established with the center monitoring point position as the coordinate origin. The x-axis points to the north direction, and the y-axis points to the east direction. The coordinate points are used to represent the position of each pixel in the monitoring grayscale image. The image construction unit uses the coordinate point in the monitoring grayscale image that has the same horizontal coordinate and vertical coordinate as the monitoring point coordinate as the trapping coordinate point, uses the coordinate point in the monitoring grayscale image that is not the trapping coordinate point as the construction coordinate point, and sets the grayscale value of the trapping coordinate point to L(x(i), y(i)), and sets the grayscale value of the construction coordinate point to L(x, y), where: , , Among them, u represents the grayscale construction parameter, 100≤u≤150, i max It represents the maximum value of the monitoring point number, and its value is equal to the number of monitoring points. (x, y) represents the coordinates of the construction coordinate point. When L(x(i), y(i))<0 or L(x, y)<0, the grayscale value of the trapping coordinate point that meets the condition is set to L(x(i), y(i))=0, and the grayscale value of the construction coordinate point that meets the condition is set to L(x, y)=0; the image construction unit analyzes the relationship between the monitoring point coordinates, forest area and quantity changes to analyze the monitoring grayscale image, construct the relationship between each information and convert it into an image form, so that the data change analysis is more intuitive, and the integrity of the system analysis data is ensured, thereby improving the system's analysis efficiency of the first monitoring information and improving the accuracy of the first monitoring information analysis. It can be understood that in this embodiment, the value of the grayscale construction parameter is not specifically limited, and the technicians in this field can set it freely, as long as the analysis of the grayscale value of the trapping coordinate point is satisfied. The optimal value of the grayscale construction parameter is: u=125.

[0033] Please continue reading Figure 3 As shown, the monitoring data construction module includes: A tree analysis unit, used to adjust the construction process of the monitoring grayscale image so that the grayscale value of the construction coordinate point is related to the change in the number of trees, and the tree analysis unit is connected to the image construction unit; The tree analysis unit adjusts the analysis process of the grayscale value of the constructed coordinate point according to the number of trees: when M(k)≥M(k-1), the tree analysis unit determines that the number of trees is stable and does not adjust the analysis process of the grayscale value of the constructed coordinate point; when M(k)<M(k-1), the tree analysis unit determines that the number of trees is reduced and adjusts the analysis process of the grayscale value of the constructed coordinate point. The grayscale value of the constructed coordinate point after adjustment is L1(x,y), and L1(x,y)=L(x,y)×log M(k-1) M(k); where M(k) represents the number of trees in the current analysis period, M(k-1) represents the number of trees in the previous analysis period, k represents the analysis period number, k∈N + Through the analysis of the number of trees by the tree analysis unit, the analysis process of the grayscale value of the constructed coordinate points is adjusted to adjust the construction process of the monitoring grayscale image, so as to increase the grayscale value of the constructed coordinate points when the trees are seriously damaged by pests and diseases and die, making the image data more obvious, thereby improving the system's analysis efficiency of the first monitoring information and improving the accuracy of the analysis of the first monitoring information.

[0034] Please continue reading Figure 3 As shown, the monitoring data construction module includes: An acquisition and analysis unit is used to optimize the adjustment process of the monitoring grayscale image so that the grayscale value of the constructed coordinate point is related to the acquisition interval; The acquisition and analysis unit optimizes the adjustment process of the grayscale value of the construction coordinate point according to the acquisition date. The grayscale value of the optimized construction coordinate point is L2(x, y), and L2(x, y)=L1(x, y)×T(i)×i max / ∑T(i), wherein T(i) represents the number of days between the acquisition date of each monitoring point in the current analysis period and its last acquisition date; the acquisition date is analyzed by the acquisition and analysis unit to optimize the adjustment process of the grayscale value of the constructed coordinate point, so as to optimize the construction process of the monitoring grayscale image and increase the diversity of the system analysis, thereby improving the system's analysis efficiency of the first monitoring information and improving the accuracy of the analysis of the first monitoring information; the acquisition and analysis unit is connected to the tree analysis unit.

[0035] Please continue reading Figure 1 As shown, the forestry monitoring point data intelligent processing system also includes: The second monitoring module is used to use the lower left corner pixel point of the second monitoring image as the coordinate origin, and the two sides adjacent to the coordinate origin as the x-axis and the y-axis, to establish a plane rectangular coordinate system, the x-axis increases from left to right, and the y-axis increases from bottom to top, and the position of each pixel point in the second monitoring image is represented by a coordinate point; The second monitoring module compares the second monitoring image with the preset blade contour, and extracts the blade area according to the comparison result: when Z≥α, the second monitoring module extracts the area corresponding to the preset blade contour in the second monitoring image as the blade area; when Z<α, the second monitoring module does not analyze the blade area; wherein Z represents the similarity between the second monitoring image and the preset blade contour, α represents the similarity threshold, 0.8≤α<1; When calculating the similarity between the second monitoring image and the preset blade profile, the second monitoring module performs binarization processing on the second monitoring image to remove the area in the second monitoring image that is significantly different from the color of the blade, calculates the curvature of the second monitoring image after the binarization processing, and compares the curvature of the second monitoring image after the binarization processing with the curvature of the preset blade profile to calculate the similarity between the second monitoring image and the preset blade profile; The second monitoring module calculates the average grayscale value of the leaf area from the grayscale value of the leaf area, which is recorded as R=∑L(X,Y) / NL, where R represents the average grayscale value of the leaf area, L(X,Y) represents the grayscale value of the leaf area, (X,Y) represents the pixel coordinates of the leaf area, and NL represents the number of pixels in the leaf area; The second monitoring module analyzes the second hazard parameter according to the grayscale value of the leaf area and the average grayscale value of the leaf area: when L(X,Y)<R×β, the second monitoring module extracts the current analysis pixel as the defective pixel, and counts the number of defective pixels as the defect number; when L(X,Y)≥R×β, the second monitoring module does not analyze the defective pixel; the second monitoring module calculates the second hazard parameter according to the defect number and the number of pixels in the leaf area, and sets B=NR / NL; wherein β represents the defect threshold, 0.3≤β≤0.7, B represents the second hazard parameter, and NR represents the defect number. It can be understood that, in the present embodiment, there is no specific limitation on the setting of the similarity threshold, and those skilled in the art can freely set it, and it only needs to satisfy the extraction of the leaf area. The optimal value of the similarity threshold is: α=0.8; in the present embodiment, there is no specific limitation on the calculation method of the similarity between the second monitoring image and the preset blade contour, and those skilled in the art can freely set it. For example, it can also be set to extract the grayscale value features and morphological features of the second monitoring image according to the grayscale value of the second monitoring image, and compare the extracted grayscale value features and morphological features with the grayscale value features and morphological features of the preset blade contour to calculate the similarity between the second monitoring image and the preset blade contour; in the present embodiment, there is no specific limitation on the value of the incompleteness threshold, and those skilled in the art can freely set it, and it only needs to satisfy the analysis of the second hazard parameter. The optimal value of the incompleteness threshold is: β=0.4; the second monitoring module is connected to the monitoring acquisition module.

[0036] Please continue reading Figure 1 As shown, the forestry monitoring point data intelligent processing system also includes: The comprehensive monitoring module is used to analyze the pest and disease parameters according to the monitoring grayscale image, to adjust the analysis process of the pest and disease parameters according to the first hazard parameter, to optimize the adjustment process of the pest and disease parameters according to the second hazard parameter, and to analyze the pest and disease level according to the pest and disease parameters. The comprehensive monitoring module is connected to the first monitoring module, the monitoring data construction module and the second monitoring module.

[0037] See also Figure 4 As shown, the comprehensive monitoring module is provided with: Pest and disease analysis unit, used to analyze pest and disease parameters and use pest and disease parameters to represent the pest and disease situation in the forest area; The pest analysis unit analyzes the pest parameters according to the monitoring grayscale image. The pest analysis unit counts the number of constructed coordinate points satisfying L(x, y)=0 in the monitoring grayscale image as the number of abnormal points, and calculates the pest parameters according to the number of abnormal points through the pest parameter analysis formula. The pest analysis unit is provided with the pest parameter analysis formula as follows: , Wherein, D represents the pest and disease parameter, N1 represents the number of pixels in the monitoring grayscale image, N2 represents the number of abnormal points, H(i) represents the height of the tree, and NH represents the number of trees; the pest and disease analysis unit analyzes the monitoring grayscale image to analyze the pest and disease parameter, and conducts a comprehensive analysis of the pest and disease situation in the forest area, thereby improving the system's analysis efficiency of the first monitoring information and improving the accuracy of the analysis of the first monitoring information.

[0038] Please continue reading Figure 4 As shown, the comprehensive monitoring module is provided with: a first hazard analysis unit, used to adjust the analysis process of the pest and disease parameter so that the adjusted pest and disease parameter is related to the hazard level of the monitored object, the first hazard analysis unit being connected to the pest and disease analysis unit; The first hazard analysis unit adjusts the analysis process of the pest parameter according to the first hazard parameter: when A(i)>P(3) / 2, the first hazard analysis unit determines that the first hazard parameter does not meet the threshold value, and adjusts the analysis process of the pest parameter. The adjusted pest parameter is D1, and D1=D×log A(i)P(3); when A(i)≤P(3) / 2, the first hazard analysis unit determines that the first hazard parameter meets the threshold value and does not adjust the analysis process of the pest and disease parameter; the first hazard analysis unit analyzes the first hazard parameter to adjust the analysis process of the pest and disease parameter, and when the data in the hazard level of the monitored object is biased towards the hazard level of the monitored object of leaf pests, the pest and disease parameter is increased, thereby improving the system's analysis efficiency of the first monitoring information and improving the accuracy of the analysis of the first monitoring information.

[0039] Please continue reading Figure 4 As shown, the comprehensive monitoring module is provided with: A second hazard analysis unit, used for optimizing the adjustment process of the pest and disease parameter so that the optimized pest and disease parameter is correlated with the second hazard parameter, the second hazard analysis unit being connected to the first hazard analysis unit; The second hazard analysis unit optimizes the adjustment process of the pest parameter according to the second hazard parameter: when B≥b, the second hazard analysis unit determines that the second hazard parameter does not meet the threshold value, optimizes the adjustment process of the pest parameter, and the optimized pest parameter is D2, setting D2=D1×e B ; When B<b, the second hazard analysis unit determines that the second hazard parameter meets the threshold value, and does not optimize the adjustment process of the pest and disease parameter; wherein b represents the leaf erosion threshold value, 0.1≤b≤0.2. Specifically, in this embodiment, the second hazard analysis unit analyzes the second hazard parameter to optimize the adjustment process of the pest and disease parameter, and increases the pest and disease parameter when the second hazard parameter is large, thereby improving the system's analysis efficiency of the first monitoring information and improving the accuracy of the analysis of the first monitoring information. It can be understood that in this embodiment, the value of the leaf erosion threshold value is not specifically limited, and those skilled in the art can freely set it as long as the optimization of the pest and disease parameter is satisfied. The optimal value of the leaf erosion threshold value is: b=0.1.

[0040] Please continue reading Figure 4 As shown, the comprehensive monitoring module is provided with: A level analysis unit, used for analyzing the level of pests and diseases, and realizing the analysis of the level of pests and diseases in the forest area, the level analysis unit is connected to the second hazard analysis unit; The level analysis unit analyzes the pest level according to the pest parameters: when D≤d1, the level analysis unit determines that the pest level is normal; when d1<D≤d2, the level analysis unit determines that the pest level is low risk; when D>d2, the level analysis unit determines that the pest level is high risk; wherein d1 represents the first pest level threshold, 0.7≤d1<1, d2 represents the second pest level threshold, 1≤d2≤1.2. It can be understood that the value of the pest level threshold is not specifically limited in this embodiment, and those skilled in the art can freely set it, as long as it meets the early warning of pests and diseases. The optimal value of the pest parameter threshold is: d1=0.8, d2=1.1.

[0041] Please continue reading Figure 1 As shown, the forestry monitoring point data intelligent processing system also includes: The level output module is used to output the pest and disease parameters, and the level output module is connected to the comprehensive monitoring module.

[0042] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the embodiments here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. A forestry monitoring point data intelligent processing system, characterized in that: include: A monitoring acquisition module is used to obtain the first monitoring information, the second monitoring information, the third monitoring information, the forest area information and the collection date of the forest pest monitoring point; A first monitoring module, used to analyze the type of the monitored object according to the first monitoring image and the first preset monitoring image, and also used to analyze the first hazard parameter according to the first monitoring information and the type of the monitored object; A monitoring data construction module, used to analyze the coordinates of the monitoring points according to the third monitoring information, to analyze the quantity change relationship according to the first monitoring information and the third monitoring information, and to construct a monitoring grayscale image according to the coordinates of the monitoring points, the forest area information, the quantity change relationship and the collection date; A second monitoring module, used for analyzing a second hazard parameter according to the second monitoring information and the third monitoring information; A comprehensive monitoring module, for analyzing pest parameters according to the monitoring grayscale image, for adjusting the analysis process of the pest parameters according to the first hazard parameter and the second hazard parameter, and for analyzing the pest level according to the pest parameters; The level output module is used to output the level of pests and diseases.

2. The forestry monitoring point data intelligent processing system according to claim 1 is characterized in that: The first monitoring module is provided with a pest analysis unit, which is used to calculate a first hazard parameter A(i) according to the type of monitored object and the hazard level of the monitored object.

3. The forestry monitoring point data intelligent processing system according to claim 2 is characterized in that: The monitoring data construction module is provided with a coordinate analysis unit, which is used to analyze the trapping coordinate point (x(i), y(i)) based on the relative distance S(i) and the relative angle θ(i).

4. The forestry monitoring point data intelligent processing system according to claim 3 is characterized in that: The monitoring data construction module is further provided with a relationship analysis unit, which is used to calculate the quantity change relationship according to the monitoring target quantity and the third monitoring information through a quantity relationship analysis formula. The relationship analysis unit is provided with a quantity relationship analysis formula as follows: Q(i)=[N(i)-N(1)] / S(i); Among them, Q(i) represents the quantity change relationship, N(i) represents the number of monitoring targets, and N(1) represents the number of monitoring targets at the central monitoring point.

5. The forestry monitoring point data intelligent processing system according to claim 4 is characterized in that: The image construction unit uses the coordinate point in the monitoring grayscale image that has the same horizontal coordinate and vertical coordinate as the monitoring point coordinate as the trapping coordinate point, uses the coordinate point in the monitoring grayscale image that is not a trapping coordinate point as the construction coordinate point, and sets the grayscale value of the trapping coordinate point to L(x(i), y(i)), and sets the grayscale value of the construction coordinate point to L(x, y).

6. The forestry monitoring point data intelligent processing system according to claim 5 is characterized in that: The monitoring data construction module is further provided with a tree analysis unit, which is used to compare the number of trees M(k) with the number of trees M(k-1) in the previous analysis cycle. When the number of trees decreases, the analysis process of the grayscale value of the construction coordinate point is adjusted, and the grayscale value of the construction coordinate point after adjustment is L1(x, y); The monitoring data construction module is also provided with a collection and analysis unit, which is used to optimize the adjustment process of the grayscale value of the construction coordinate point according to the collection date, and the optimized grayscale value of the construction coordinate point is L2 (x, y).

7. The forestry monitoring point data intelligent processing system according to claim 6 is characterized in that: The second monitoring module compares the similarity Z between the second monitoring image and the preset blade profile with the similarity threshold α, and extracts the blade area according to the comparison result; The second monitoring module calculates the average gray value R of the leaf area based on the gray value of the leaf area; The second monitoring module analyzes the second hazard parameter according to the gray value of the leaf area and the average gray value R of the leaf area: when L(X,Y)<R×β, the second monitoring module extracts the current analysis pixel as the defective pixel, and counts the number of the defective pixel as the defective number; When L(X,Y)≥R×β, the second monitoring module does not analyze the incomplete pixels; The second monitoring module calculates the second hazard parameter B according to the number of defects and the number of pixel points in the leaf area.

8. The forestry monitoring point data intelligent processing system according to claim 7 is characterized in that: The comprehensive monitoring module is provided with a pest and disease analysis unit, which is used to analyze pest and disease parameters according to the monitoring grayscale image. The pest and disease analysis unit counts the number of constructed coordinate points in the monitoring grayscale image that satisfy L(x, y)=0 as the number of abnormal points N2, and calculates the pest and disease parameter D according to the number of abnormal points.

9. The forestry monitoring point data intelligent processing system according to claim 8 is characterized in that: The integrated monitoring module is further provided with a first hazard analysis unit, which is used to adjust the analysis process of the pest parameter according to the first hazard parameter A(i). When the first hazard parameter does not meet the threshold, the analysis process of the pest parameter is adjusted, and the adjusted pest parameter is D1; The integrated monitoring module is also provided with a second hazard analysis unit, which is used to compare the second hazard parameter B with the leaf erosion threshold b. When the second hazard parameter does not meet the threshold, the adjustment process of the pest parameter is optimized, and the optimized pest parameter is D2.

10. The forestry monitoring point data intelligent processing system according to claim 9, characterized in that: The comprehensive monitoring module is also provided with a level analysis unit for comparing the pest parameter D with the pest level thresholds d1 and d2, and determining the pest level according to the comparison result, wherein the pest level includes normal, low risk and high risk.

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