Paris polyphylla planting pest control scheme data analysis system and method

Abstract

The invention relates to the technical field of paris polyphylla plant disease and insect pest data processing, and discloses a paris polyphylla planting plant disease and insect pest control scheme data analysis system and method. According to paris polyphylla plant growth image shelter type identification information, an intelligent search algorithm and standard preset paris polyphylla plant growth image shelter removal algorithm type information are combined to carry out shelter feature removal algorithm type accurate analysis in a paris polyphylla plant growth image; scientific matching of image shelter removal algorithm objects based on shelter interference feature types in paris polyphylla disease and insect pest monitoring images is realized, and the quality of paris polyphylla disease and insect pest monitoring image acquisition is improved; the method can autonomously and efficiently remove interference features of plant surface attached shelters in the paris polyphylla plant growth image, realizes efficient and accurate processing of the interference features of the shelters in the paris polyphylla plant growth image, improves the authenticity of paris polyphylla plant disease and insect pest monitoring image acquisition, and improves the paris polyphylla planting disease and insect pest control effect.

Description

Technical Field

[0001] The present invention relates to the technical field of Paris polyphylla disease and insect pest data processing, and in particular to a Paris polyphylla planting disease and insect pest control program data analysis system and method. Background Art

[0002] Paris polyphylla is a general term for Paris polyphylla plants of the genus Paris polyphylla of the family Trillium, and is a perennial herb. The Paris polyphylla planting and growth process requires regular monitoring of pests and diseases in the planting fields; the existing Paris polyphylla planting pest and disease supervision operations directly collect Paris polyphylla growth status images for Paris polyphylla planting pest and disease analysis. Due to the obstructions such as water, soil particles, dust, and dead leaves attached to the surface of Paris polyphylla plants in the natural environment, the collected Paris polyphylla growth status images are easily distorted, resulting in inaccurate results of Paris polyphylla planting pest and disease analysis, and it is also impossible to accurately analyze the Paris polyphylla planting pest and disease prevention and control plan, which reduces the quality and efficiency of Paris polyphylla planting pest and disease prevention and control operations. Summary of the invention

[0003] 1. Technical issues to be resolved In order to solve the problem that the existing Paris polyphylla planting pest and disease supervision operations directly collect Paris polyphylla growth status images for Paris polyphylla planting pest and disease analysis, the collected Paris polyphylla growth status images are easily distorted due to obstructions such as moisture, soil particles, dust, and dead leaves attached to the surface of Paris polyphylla plants in the natural environment, resulting in inaccurate Paris polyphylla planting pest and disease analysis results, and it is also impossible to accurately analyze the Paris polyphylla planting pest and disease prevention and control plan, thereby reducing the quality and efficiency of Paris polyphylla planting pest and disease prevention operations. The above purposes are achieved by accurately collecting Paris polyphylla plant growth image information, intelligently identifying the types of obstructions in Paris polyphylla plant growth images, scientifically matching Paris polyphylla plant growth image occlusion removal algorithm objects, accurately generating Paris polyphylla plant growth image preprocessing information, scientifically identifying the types of Paris polyphylla plant diseases and pests, accurately analyzing Paris polyphylla plant disease and pest prevention and control methods, and intuitively and visually outputting Paris polyphylla planting pest and disease prevention and control analysis result information.

[0004] (II) Technical solution The present invention is implemented by the following technical scheme: a data analysis method for Paris polyphylla planting pest control scheme, the method comprising the following steps: S1. Collecting Paris polyphylla plant growth image data; S2, performing identification processing on the type of obstructions attached to the plant surface in the Paris polyphylla plant growth image collected according to the Paris polyphylla plant growth image data and the Paris polyphylla plant surface obstruction image data, generating Paris polyphylla plant growth image obstruction type identification data, and directly executing step S5 when there is no obstruction; S3, when there is an obstruction, performing a matching process of an algorithm type for eliminating obstructions attached to the plant surface in the Paris polyphylla plant growth image according to the Paris polyphylla plant growth image obstruction type identification data and the Paris polyphylla plant growth image obstruction elimination algorithm type data, and generating Paris polyphylla plant growth image obstruction elimination algorithm analysis data; S4, performing preprocessing of removing obstructions attached to the plant surface in the Paris polyphylla plant growth image according to the Paris polyphylla plant growth image data and the Paris polyphylla plant growth image obstruction removal algorithm analysis data, and generating Paris polyphylla plant growth image preprocessing data; S5, performing identification processing of the pest and disease type of the Paris polyphylla plant on the Paris polyphylla plant growth image data or the Paris polyphylla plant growth image preprocessing data and the Paris polyphylla plant pest and disease image data to generate Paris polyphylla plant pest and disease type identification data, and directly executing step S7 when there is no pest and disease; S6. When pests and diseases exist, analyzing and processing the pest and disease control methods of the Paris polyphylla plants according to the Paris polyphylla plant pest and disease type identification data and the Paris polyphylla plant pest and disease control method data to generate Paris polyphylla plant pest and disease control method analysis data; S7. Construct the data of the analysis results of the pest and disease control in Paris polyphylla cultivation and perform feedback work on the analysis results of the pest and disease control in Paris polyphylla cultivation.

[0005] Preferably, the operation steps of collecting Paris polyphylla plant growth image data are as follows: S11, collecting the growth image information of the Paris polyphylla plants in the natural state in the Paris polyphylla planting field online through a shooting lens, and generating a Paris polyphylla plant growth image data set ;in represents the yth collected image data of Paris polyphylla plant growth, Indicates the maximum number of Paris polyphylla plant growth images.

[0006] The present invention accurately and efficiently collects growth image information of Paris polyphylla plants through a shooting lens, thereby achieving the effect of providing reliable data support for subsequent accurate analysis of Paris polyphylla plant disease and insect pest information.

[0007] Preferably, the type of obstruction attached to the plant surface in the Paris polyphylla plant growth image collected based on the Paris polyphylla plant growth image data and the Paris polyphylla plant surface obstruction image data is identified and processed to generate the Paris polyphylla plant growth image obstruction type identification data. When there is no obstruction, the operation steps of directly executing step S5 are as follows: S21. Establishing a data set of images of obstructions attached to the surface of Paris polyphylla plants , ;in represents the image data of the obstruction attached to the surface of the Paris polyphylla plant corresponding to the oth Paris polyphylla plant obstruction type, Indicates the maximum value of the number of types of obstructions of the Paris polyphylla plant, wherein the types of obstructions of the Paris polyphylla plant include water drop obstructions, soil particle obstructions, dust obstructions and dead leaf obstructions; the image data of obstructions attached to the surface of the Paris polyphylla plant represents real-scene feature image data of different types of obstructions attached to the surface of the Paris polyphylla plant in a natural state; S22, using a KD tree nearest neighbor search algorithm to search the Paris polyphylla plant growth image data set A for the Paris polyphylla plant growth image data set A. The image data set B of the obstruction object attached to the surface of the Paris polyphylla plant Perform image feature matching and generate Paris polyphylla plant growth image occlusion type recognition data based on the image feature matching results ; when and If the row image feature matching is successful, it means that there is the oth type of Paris polyphylla plant occlusion attached to the surface of the Paris polyphylla plant, then the Paris polyphylla plant growth image occlusion type recognition data is output If there is an obstruction, the type of obstruction information of the Paris polyphylla plant is output; when and If the image features are not matched successfully, it means that there is no obstruction on the surface of the Paris polyphylla plant, then the Paris polyphylla plant growth image obstruction type recognition data is output. If there is no obstruction, then directly execute step S5.

[0008] The present invention achieves the effect of scientifically identifying the interference characteristics of obstructions in the Paris polyphylla plant disease and insect pest monitoring images by performing intelligent analysis of the types of obstructions in the Paris polyphylla plant growth images based on the Paris polyphylla plant growth image information in combination with the KD tree nearest neighbor search algorithm and the scientifically stored image information of obstructions attached to the surface of the Paris polyphylla plant.

[0009] Preferably, when there is an obstruction, a matching process of an algorithm type for eliminating obstructions attached to the plant surface in the Paris polyphylla plant growth image is performed according to the Paris polyphylla plant growth image obstruction type identification data and the Paris polyphylla plant growth image obstruction elimination algorithm type data, and the operating steps for generating the Paris polyphylla plant growth image obstruction elimination algorithm analysis data are as follows: S31, the Paris polyphylla plant growth image occlusion type identification data When there are occluders, establish a data set of occluder removal algorithm types for Paris polyphylla plant growth images ,in Represents the Paris polyphylla plant growth image occluder culling algorithm type data corresponding to the oth Paris polyphylla plant occluder type; the Paris polyphylla plant growth image occluder culling algorithm type data represents the optimal image occluder culling algorithm type information set for different types of occluders attached to the Paris polyphylla plant surface; the image occluder culling algorithm types include rasterization-based occlusion culling algorithm, BSP tree culling algorithm, PVS culling algorithm and ray tracing culling algorithm; S32, using the BERT language model algorithm to identify the type of occlusion in the Paris polyphylla plant growth image A data set of the type of occluder removal algorithm for the Paris polyphylla plant growth image Type data of the occluder removal algorithm for the Paris polyphylla plant growth image Perform keyword matching of Paris polyphylla plant occlusion type, and search for Paris polyphylla plant growth image occlusion type identification data Corresponding Paris polyphylla plant growth image occlusion removal algorithm type data , and generate the occlusion removal algorithm analysis data of Paris polyphylla plant growth image through data identification .

[0010] The present invention performs a precise analysis of the type of occluder feature removal algorithm in the Paris polyphylla plant growth image according to the occluder type identification information of the Paris polyphylla plant growth image, combining the BERT language model algorithm with the standard preset Paris polyphylla plant growth image occluder removal algorithm type information, so as to achieve the effect of scientifically matching the image occluder removal algorithm object based on the occluder interference feature type in the Paris polyphylla pest and disease monitoring image.

[0011] Preferably, the steps of performing preprocessing for removing occluders attached to the plant surface in the Paris polyphylla plant growth image based on the Paris polyphylla plant growth image data and the Paris polyphylla plant growth image occluder removal algorithm analysis data, and generating the Paris polyphylla plant growth image preprocessing data are as follows: S41, using the Paris polyphylla plant growth image occlusion removal algorithm to analyze data The corresponding image occlusion removal algorithm is used to remove the Paris polyphylla plant growth image data set A. According to the Paris polyphylla plant growth image number, the Paris polyphylla plant growth image is sequentially preprocessed to remove the obstructions attached to the plant surface, and a Paris polyphylla plant growth image preprocessing data set is generated. ,in Represents the preprocessing data of the y-th Paris polyphylla plant growth image.

[0012] The present invention achieves the effect of efficiently and accurately processing the interference features of occluders in the Paris polyphylla pest and disease monitoring image by autonomously and efficiently performing preprocessing to remove interference features of occluders attached to the plant surface in the Paris polyphylla plant growth image.

[0013] Preferably, the Paris polyphylla plant growth image data or the Paris polyphylla plant growth image preprocessing data and the Paris polyphylla plant pest and disease image data are processed to identify the pest and disease type of the Paris polyphylla plant to generate Paris polyphylla plant pest and disease type identification data. When there is no pest and disease, the operation steps of directly executing step S7 are as follows: S51. Establishing a data set of plant diseases and insect pests images of Paris polyphylla ;in represents the image data of Paris polyphylla plant diseases and insect pests corresponding to the kth Paris polyphylla plant disease and insect pest type, Indicates the maximum value of the number of pest types of Paris polyphylla plants, the pest types of Paris polyphylla plants include beetle larvae pest types, cutworm pest types, wireworm pest types, leaf miner pest types, damping-off pest types, stem rot pest types, leaf spot pest types, and brown spot pest types; the Paris polyphylla plant pest image data indicates standard image data corresponding to the symptom phenotypes of different types of pests and diseases of Paris polyphylla plants; S52, using a unified cost search algorithm to generate the Paris polyphylla plant growth image data set A. Or the Paris polyphylla plant growth image preprocessing data set The Paris polyphylla plant growth image preprocessing data The Paris polyphylla plant pest and disease image data set C Perform image feature matching and generate pest and disease type identification data of Paris polyphylla plants based on the image feature matching results ; when or and If the image features are not matched successfully, it means that the Paris polyphylla plant has pests and diseases, then the Paris polyphylla plant pest and disease type identification data is output. If there are no pests and diseases, directly execute step S7.

[0014] The present invention scientifically and accurately identifies the types of pests and diseases of Paris polyphylla plants by combining Paris polyphylla plant growth image information or Paris polyphylla plant growth image preprocessing information with a unified cost search algorithm and Paris polyphylla plant disease and insect pest image information based on big data storage, thereby achieving the effect of intelligent and precise monitoring of diseases and pests in the Paris polyphylla planting process.

[0015] Preferably, when pests and diseases exist, the prevention and control methods of pests and diseases of the Paris polyphylla plant are analyzed and processed according to the Paris polyphylla plant pest and disease type identification data and the Paris polyphylla plant pest and disease control method data, and the operation steps for generating the Paris polyphylla plant pest and disease control method analysis data are as follows: S61, when the Paris polyphylla plant pest type identification data To establish a data set of pest control methods for Paris polyphylla plants when pests and diseases exist ,in Represents the Paris polyphylla plant disease and insect pest control method data corresponding to the k-th Paris polyphylla plant disease and insect pest type, wherein the Paris polyphylla plant disease and insect pest control method data represents the optimal disease and insect pest control treatment process and control equipment information set according to different types of Paris polyphylla plant disease and insect pest standards; S62, identifying the pest and disease type data of the Paris polyphylla plant Data set on pest control methods for Paris polyphylla plants Data on pest control methods for Paris polyphylla plants Character matching of the pest and disease type of Paris polyphylla is performed, and the Paris polyphylla plant pest and disease control method analysis data D is constructed. The specific operation steps for generating the Paris polyphylla plant pest and disease control method analysis data D are as follows: S621, initialization, update the maximum number of iterations T of the algorithm and randomly initialize the prevention and control method in the optimization space to identify the position of the osprey population. The position initialization formula is: ,in It indicates that the prevention and control method identifies the position of osprey i in the j-dimensional space, that is, the prevention and control method identifies the position of osprey i in the space dimension Data set of pest control methods for Paris polyphylla plants The position in the search space, To find the optimal lower boundary, that is, the data set of the pest control method of Paris polyphylla plant The lower bound in the search space, To find the optimal upper boundary, that is, the data set of pest control methods for Paris polyphylla plants The upper boundary in the search space, r represents a random number in the interval [0,1]; S622, exploration stage, the exploration stage of updating the population of ospreys is based on the simulation of the natural behavior of ospreys, and the ospreys are identified in the data set of pest control methods of the Paris polyphylla plants. Randomly detected in the search space the identification data of pests and diseases types of the Paris polyphylla plants Matching data on pest control methods for Paris polyphylla plants The target position is attacked, and the corresponding control method is updated to identify the new position of the osprey based on the simulated control method identification osprey moving towards the target. The control method identification osprey position update formula ,in It indicates the position of osprey i in the j-dimensional space after the prevention and control method is updated, that is, the spatial dimension of osprey i after the prevention and control method is updated is Data set of pest control methods for Paris polyphylla plants Position in the search space; Represents the data set of pest control methods for identifying diseases and insect pests of Osprey in the Paris polyphylla plant Search the search space to find the identification data of pest and disease types related to the Paris polyphylla plant Matching data on pest control methods for Paris polyphylla plants The location of the target; represents a constant with a value of 1 or 2; if the updated new position is better, the initial position before the update of the prevention and control method to identify the osprey is replaced according to the exploration stage position replacement formula. The exploration stage position replacement formula is ,in It represents the optimal position of osprey i in the j-dimensional space identified by the prevention and control method in the exploration stage, that is, the spatial dimension of osprey i after the prevention and control method in the exploration stage is updated Data set of pest control methods for Paris polyphylla plants Search the search space to find the identification data of pest and disease types related to the Paris polyphylla plant The most matching data on pest control methods for the Paris polyphylla plant location; express Data on pest control methods for the Paris polyphylla plants at the location Data on identification of pests and diseases of the Paris polyphylla plant The fitness value of express Data on pest control methods for the Paris polyphylla plants at the location Data on identification of pests and diseases of the Paris polyphylla plant The fitness value of S623, development stage, prevention and control method identification, osprey in the Paris polyphylla plant disease and insect pest prevention method data set Hunting and eating pests and diseases type identification data of the Paris polyphylla plant in the search space Matching data on pest control methods for Paris polyphylla plants Objective: The development phase of the algorithm for the update of the osprey population is based on modeling the simulation of the natural behavior of the osprey, and calculating new random positions as suitable for edible and pest identification data of the Paris polyphylla plant. Matching data on pest control methods for Paris polyphylla plants Target location, calculation of new edible and pest identification data of the Paris polyphylla plant Matching data on pest control methods for Paris polyphylla plants The formula for the target's position ,in It means that the new random position of osprey i in the j-dimensional space after the control method is updated is the position suitable for edible fish, that is, the space dimension of osprey i after the control method is updated is Data set of pest control methods for Paris polyphylla plants Searching for new random positions in the space as suitable for edible plant pest and disease type identification data Matching data on pest control methods for Paris polyphylla plants The position of the target, t represents the number of iterations of the current algorithm; if the value of the objective function is improved at this new position, the initial position before the update of the prevention and control method to identify the osprey is replaced according to the development stage position replacement formula, and the development stage position replacement formula is ,in It represents the optimal position of osprey i in the j-dimensional space after the prevention and control method is identified in the development stage, that is, the spatial dimension of osprey i after the prevention and control method is updated in the development stage is Data set of pest control methods for Paris polyphylla plants Search for the best position in the space; express Data on pest control methods for the Paris polyphylla plants at the location Data on identification of pests and diseases of the Paris polyphylla plant The fitness value of S624: When the algorithm meets the maximum number of iterations, output the identification data of the pest and disease type of the Paris polyphylla plant. The most matching data on pest control methods for the Paris polyphylla plant , otherwise continue to execute steps S622 to S623 until the maximum number of iterations is met; S625, the Paris polyphylla plant disease and insect pest control method data output in step S624 After data identification, analysis data on pest and disease control methods for Paris polyphylla plants were constructed.

[0016] The present invention achieves the effect of scientific matching of the pest and disease control plan in the Paris polyphylla planting process by accurately analyzing the pest and disease control method of the Paris polyphylla plant according to the pest and disease type identification information of the Paris polyphylla plant combined with the Osprey optimization algorithm and the scientifically preset pest and disease control method information of the Paris polyphylla plant.

[0017] Preferably, the steps of constructing the Paris polyphylla planting pest control analysis result data and performing the Paris polyphylla planting pest control analysis result feedback operation are as follows: S71, generating the Paris polyphylla plant growth image data set A or the Paris polyphylla plant growth image preprocessing data set , identification data of pests and diseases of Paris polyphylla plants , the Paris polyphylla plant disease and insect pest control method analysis data D is collected and combined, and Paris polyphylla planting disease and insect pest control analysis result data H is constructed, wherein ; S72, pushing the Paris polyphylla planting disease and insect pest control analysis result data H online to the Paris polyphylla planting disease and insect pest monitoring terminal through the Internet of Things communication network and performing the Paris polyphylla planting disease and insect pest control analysis result feedback output operation in conjunction with the display screen.

[0018] The present invention achieves the effects of accurate and standardized collection of pest and disease control monitoring information during the Paris polyphylla planting process and visual output of pest and disease control monitoring information during the Paris polyphylla planting process by efficiently and accurately constructing pest and disease control analysis result information for the Paris polyphylla planting, and timely and efficiently pushing the pest and disease control analysis result information for the Paris polyphylla planting to a Paris polyphylla planting pest and disease monitoring terminal through an Internet of Things communication network, and performing intuitive and clear feedback output in conjunction with a display screen.

[0019] A Paris polyphylla planting pest and disease prevention scheme data analysis system, used to implement the Paris polyphylla planting pest and disease prevention scheme data analysis method, the system comprising a Paris polyphylla planting pest and disease image acquisition module, a Paris polyphylla planting pest and disease analysis module, and a Paris polyphylla planting pest and disease analysis result output module; The Paris polyphylla planting pest and disease image acquisition module includes a Paris polyphylla plant growth image acquisition unit, a Paris polyphylla plant surface attached obstruction image storage unit, a Paris polyphylla plant growth image obstruction type recognition unit, a Paris polyphylla plant growth image obstruction removal algorithm storage unit, a Paris polyphylla plant growth image obstruction removal algorithm matching unit, and a Paris polyphylla plant growth image obstruction preprocessing unit; The Paris polyphylla plant growth image acquisition unit is used to acquire Paris polyphylla plant growth image data through a shooting lens; the Paris polyphylla plant surface attached obstruction image storage unit is used to store Paris polyphylla plant surface attached obstruction image data; the Paris polyphylla plant growth image obstruction type recognition unit is used to perform plant surface attached obstruction type recognition processing in the Paris polyphylla plant growth image acquired based on the Paris polyphylla plant growth image data and the Paris polyphylla plant surface attached obstruction image data, and generate Paris polyphylla plant growth image obstruction type recognition data; the Paris polyphylla plant growth image obstruction removal algorithm storage unit is used to store the Paris polyphylla plant growth image obstruction removal algorithm type. data; the Paris polyphylla plant growth image occlusion removal algorithm matching unit performs a removal algorithm type matching process on occlusions attached to the plant surface in the Paris polyphylla plant growth image according to the Paris polyphylla plant growth image occlusion type identification data and the Paris polyphylla plant growth image occlusion removal algorithm type data, and generates Paris polyphylla plant growth image occlusion removal algorithm analysis data; the Paris polyphylla plant growth image occlusion preprocessing unit performs a removal preprocessing process on occlusions attached to the plant surface in the Paris polyphylla plant growth image according to the Paris polyphylla plant growth image data and the Paris polyphylla plant growth image occlusion removal algorithm analysis data, and generates Paris polyphylla plant growth image preprocessing data; The Paris polyphylla planting pest and disease analysis module includes a Paris polyphylla plant pest and disease image storage unit, a Paris polyphylla plant pest and disease type identification unit, a Paris polyphylla plant pest and disease control method storage unit, and a Paris polyphylla plant pest and disease control program analysis unit; The Paris polyphylla plant pest and disease image storage unit is used to store Paris polyphylla plant pest and disease image data; the Paris polyphylla plant pest and disease type identification unit performs Paris polyphylla plant pest and disease type identification processing on the Paris polyphylla plant growth image data or the Paris polyphylla plant growth image preprocessing data and the Paris polyphylla plant pest and disease image data to generate Paris polyphylla plant pest and disease type identification data; the Paris polyphylla plant pest and disease control method storage unit is used to store Paris polyphylla plant pest and disease control method data; the Paris polyphylla plant pest and disease control scheme analysis unit performs Paris polyphylla plant pest and disease control method analysis processing on the Paris polyphylla plant pest and disease type identification data and the Paris polyphylla plant pest and disease control method data to generate Paris polyphylla plant pest and disease control method analysis data; The Paris polyphylla planting pest and disease analysis result output module includes a Paris polyphylla planting pest and disease control analysis result construction unit and a Paris polyphylla planting pest and disease control feedback unit; The Paris polyphylla planting disease and pest control analysis result construction unit is used to construct Paris polyphylla planting disease and pest control analysis result data; the Paris polyphylla planting disease and pest control feedback unit pushes the Paris polyphylla planting disease and pest control analysis result data to the Paris polyphylla planting disease and pest monitoring terminal online based on the Paris polyphylla planting disease and pest control analysis result data in combination with the Internet of Things communication network and executes the Paris polyphylla planting disease and pest control analysis result feedback output operation through the display screen.

[0020] (III) Beneficial effects The present invention provides a data analysis system and method for pest control schemes for Paris polyphylla. It has the following beneficial effects: 1. Accurately and efficiently collect the growth image information of Paris polyphylla plants through the shooting lens, and provide reliable data support for the subsequent accurate analysis of Paris polyphylla plant disease and pest information; intelligently analyze the types of obstructions in the Paris polyphylla plant growth images based on the Paris polyphylla plant growth image information combined with the intelligent search algorithm and the scientifically stored image information of obstructions attached to the surface of the Paris polyphylla plant, so as to scientifically identify the interference characteristics of obstructions in the Paris polyphylla disease and pest monitoring images, and improve the accuracy of identifying diseases and pests in Paris polyphylla planting; combine the Paris polyphylla plant growth image growth image obstruction type identification information with the intelligent search algorithm and the standard preset The type information of the growth image occlusion removal algorithm is used to accurately analyze the type of the occlusion feature removal algorithm in the growth image of the Paris polyphylla plant, and scientifically match the image occlusion removal algorithm object based on the occlusion interference feature type in the Paris polyphylla pest and disease monitoring image, thereby improving the quality of the Paris polyphylla pest and disease monitoring image acquisition; autonomously and efficiently perform the preprocessing of the interference feature removal of occlusions attached to the plant surface in the Paris polyphylla plant growth image, and realize the efficient and accurate processing of the occlusion interference features in the Paris polyphylla pest and disease monitoring image, thereby improving the authenticity of the Paris polyphylla pest and disease monitoring image acquisition, and improving the Paris polyphylla pest and disease control effect; Second, scientifically and accurately identify the types of pests and diseases of Paris polyphylla plants by combining Paris polyphylla plant growth image information or Paris polyphylla plant growth image preprocessing information with intelligent search algorithms and Paris polyphylla plant pest and disease image information based on big data storage, thereby realizing intelligent and precise monitoring of pests and diseases in the Paris polyphylla planting process; accurately analyze the Paris polyphylla plant pest and disease control methods based on the Paris polyphylla plant pest and disease type identification information combined with intelligent identification algorithms and scientifically preset Paris polyphylla plant pest and disease control method information, thereby realizing scientific matching of pest and disease control plans in the Paris polyphylla planting process, improving the efficiency and reliability of pest and disease control in the Paris polyphylla planting process, and realizing intelligent and digital management of Paris polyphylla planting operations; 3. By efficiently and accurately constructing the information on the analysis results of pest and disease control in Paris planting based on the growth images or preprocessed images of Paris plants, the analysis results of pests and diseases of Paris plants, and the analysis results of pest and disease control methods of Paris plants, the accurate and standardized collection of pest and disease control monitoring information in the Paris planting process can be achieved; the information on the analysis results of pest and disease control in Paris planting can be pushed to the Paris planting pest and disease monitoring terminal in a timely and efficient manner through the Internet of Things communication network, and the display screen can be used for intuitive and clear feedback output, thereby improving the efficiency and quality of pest and disease control monitoring in the Paris planting process. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of a module of a data analysis system for a Paris polyphylla planting pest control program provided by the present invention; Figure 2A flow chart of a data analysis method for a Paris polyphylla planting pest and disease control program provided by the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] The embodiments of the data analysis system and method for Paris polyphylla planting pest control scheme are as follows: Example 1 Please refer to Figure 1 - Figure 2 , a data analysis method for Paris polyphylla planting pest control program, the method comprising the following steps: S1. Collecting Paris polyphylla plant growth image data; S2, performing identification processing on the type of obstructions attached to the plant surface in the Paris polyphylla plant growth image collected based on the Paris polyphylla plant growth image data and the Paris polyphylla plant surface obstruction image data, generating Paris polyphylla plant growth image obstruction type identification data, and directly executing step S5 when there is no obstruction; S3, when there is an occluder, a matching process of the type of algorithm for removing occluders attached to the plant surface in the Paris polyphylla plant growth image is performed according to the Paris polyphylla plant growth image occluder type recognition data and the Paris polyphylla plant growth image occluder removal algorithm type data, to generate Paris polyphylla plant growth image occluder removal algorithm analysis data; S4, performing preprocessing of removing obstructions attached to the plant surface in the Paris polyphylla plant growth image according to the Paris polyphylla plant growth image data and the Paris polyphylla plant growth image obstruction removal algorithm analysis data, and generating Paris polyphylla plant growth image preprocessing data; S5, performing identification processing of the pest and disease type of the Paris polyphylla plant on the Paris polyphylla plant growth image data or the Paris polyphylla plant growth image preprocessing data and the Paris polyphylla plant pest and disease image data to generate Paris polyphylla plant pest and disease type identification data, and when there is no pest and disease, directly executing step S7; S6. When pests and diseases exist, the prevention and control methods of the pests and diseases of the Paris polyphylla plants are analyzed and processed according to the Paris polyphylla plant pest and disease type identification data and the Paris polyphylla plant pest and disease control method data to generate Paris polyphylla plant pest and disease control method analysis data; S7. Construct the data of the analysis results of the pest and disease control in Paris polyphylla cultivation and perform feedback work on the analysis results of the pest and disease control in Paris polyphylla cultivation.

[0024] For further information, see Figure 1 - Figure 2The steps for collecting Paris polyphylla plant growth image data are as follows: S11, collecting the growth image information of the Paris polyphylla plants in the natural state in the Paris polyphylla planting field online through a shooting lens, and generating a Paris polyphylla plant growth image data set ;in represents the yth collected image data of Paris polyphylla plant growth, Indicates the maximum number of Paris polyphylla plant growth images.

[0025] According to the Paris polyphylla plant growth image data and the Paris polyphylla plant surface attached obstruction image data, the type of obstruction attached to the plant surface in the Paris polyphylla plant growth image collected is identified and processed to generate the Paris polyphylla plant growth image obstruction type identification data. When there is no obstruction, the operation steps of directly executing step S5 are as follows: S21. Establishing a data set of images of obstructions attached to the surface of Paris polyphylla plants , ;in represents the image data of the obstruction attached to the surface of the Paris polyphylla plant corresponding to the oth Paris polyphylla plant obstruction type, Indicates the maximum value of the number of types of obstructions of the Paris polyphylla plant, which include water drop obstructions, soil particle obstructions, dust obstructions and dead leaf obstructions; the image data of obstructions attached to the surface of the Paris polyphylla plant indicates the real-scene feature image data of different types of obstructions attached to the surface of the Paris polyphylla plant in a natural state; S22, using the KD tree nearest neighbor search algorithm to search the Paris polyphylla plant growth image data set A The image data of the obstructions attached to the surface of the Paris polyphylla plant in the image data set B Perform image feature matching and generate Paris polyphylla plant growth image occlusion type recognition data based on the image feature matching results ; when and If the row image feature matching is successful, it means that there is the oth type of Paris polyphylla plant occlusion attached to the surface of the Paris polyphylla plant, then the Paris polyphylla plant growth image occlusion type recognition data is output If there is an obstruction, the type of obstruction information of the Paris polyphylla plant is output; when and If the image features are not matched successfully, it means that there is no obstruction on the surface of the Paris polyphylla plant, then the Paris polyphylla plant growth image obstruction type recognition data is output. If there is no obstruction, then directly execute step S5.

[0026] When there is an occluder, the occluder type identification data of the Paris polyphylla plant growth image and the occluder removal algorithm type data of the Paris polyphylla plant growth image are used to match the occluder attachment to the plant surface in the Paris polyphylla plant growth image, and the steps for generating the Paris polyphylla plant growth image occluder removal algorithm analysis data are as follows: S31. Paris polyphylla plant growth image occlusion type recognition data When there are occluders, establish a data set of occluder removal algorithm types for Paris polyphylla plant growth images ,in Represents the Paris polyphylla plant growth image occluder culling algorithm type data corresponding to the oth Paris polyphylla plant occluder type; the Paris polyphylla plant growth image occluder culling algorithm type data represents the optimal image occluder culling algorithm type information set for different types of occluders attached to the Paris polyphylla plant surface; the image occluder culling algorithm types include rasterization-based occlusion culling algorithm, BSP tree culling algorithm, PVS culling algorithm and ray tracing culling algorithm; S32, using BERT language model algorithm to identify the type of occlusion in the Paris polyphylla plant growth image Data set of occluder removal algorithm type for Paris polyphylla plant growth image Type data of occlusion removal algorithm for the growth image of the Chinese Polygonum multiflorum plant Perform keyword matching of Paris polyphylla plant occlusion type, and search for Paris polyphylla plant growth image occlusion type identification data Corresponding Paris polyphylla plant growth image occlusion removal algorithm type data , and generate the occlusion removal algorithm analysis data of Paris polyphylla plant growth image through data identification .

[0027] The steps of preprocessing the occluders attached to the plant surface in the Paris polyphylla plant growth image and generating the Paris polyphylla plant growth image preprocessing data are as follows: S41. Analyze data using the Paris polyphylla plant growth image occlusion removal algorithm The corresponding image occlusion removal algorithm is used for the Paris polyphylla plant growth image data set A. According to the Paris polyphylla plant growth image number, the Paris polyphylla plant growth image is sequentially preprocessed to remove the obstructions attached to the plant surface, and a Paris polyphylla plant growth image preprocessing data set is generated. ,in Represents the preprocessing data of the y-th Paris polyphylla plant growth image.

[0028] The Paris polyphylla plant growth image acquisition unit uses a camera to accurately and efficiently collect Paris polyphylla plant growth image information, providing reliable data support for the subsequent accurate analysis of Paris polyphylla plant disease and pest information; the Paris polyphylla plant growth image occlusion type recognition unit performs intelligent analysis of the occlusion type in the Paris polyphylla plant growth image based on the Paris polyphylla plant growth image information combined with an intelligent search algorithm and scientifically stored Paris polyphylla plant surface attached occlusion image information, thereby scientifically identifying the interference features of occlusions in Paris polyphylla disease and pest monitoring images and improving the accuracy of Paris polyphylla planting disease and pest recognition; the Paris polyphylla plant growth image occlusion rejection algorithm matching unit uses the Paris polyphylla plant growth image occlusion type recognition information combination to identify the occlusion type in the Paris polyphylla plant growth image. The intelligent search algorithm is combined with the standard preset type information of the occlusion removal algorithm of the Paris polyphylla plant growth image to perform a precise analysis of the type of the occlusion feature removal algorithm in the Paris polyphylla plant growth image, so as to scientifically match the image occlusion removal algorithm object based on the occlusion interference feature type in the Paris polyphylla pest and disease monitoring image, and improve the quality of the Paris polyphylla pest and disease monitoring image acquisition; the Paris polyphylla plant growth image occlusion preprocessing unit independently and efficiently performs the preprocessing of the interference feature removal of occlusions attached to the plant surface in the Paris polyphylla plant growth image, so as to achieve efficient and precise processing of the occlusion interference features in the Paris polyphylla pest and disease monitoring image, improve the authenticity of the Paris polyphylla pest and disease monitoring image acquisition, and improve the pest and disease control effect of Paris polyphylla planting.

[0029] For further information, see Figure 1 - Figure 2 , the Paris polyphylla plant growth image data or the Paris polyphylla plant growth image preprocessing data and the Paris polyphylla plant disease and insect pest image data are processed to identify the disease and insect pest type of the Paris polyphylla plant, and the Paris polyphylla plant disease and insect pest type identification data is generated. When there is no disease and insect pest, the operation steps of directly executing step S7 are as follows: S51. Establishing a data set of plant diseases and insect pests images of Paris polyphylla ; in represents the image data of Paris polyphylla plant diseases and insect pests corresponding to the kth Paris polyphylla plant disease and insect pest type, Indicates the maximum number of pest types of Paris polyphylla, which include beetle larvae pest types, cutworm pest types, wireworm pest types, leaf miner pest types, damping-off pest types, stem rot pest types, leaf spot pest types, and brown spot pest types; Paris polyphylla pest image data indicates the standard image data corresponding to the symptom phenotypes of different types of pests and diseases of Paris polyphylla; S52, using a unified cost search algorithm to generate the Paris polyphylla plant growth image data set A. Or Paris polyphylla plant growth image preprocessing data set Preprocessing data of growth images of Rhizoma Paridis The image data of Paris polyphylla plant diseases and insect pests in the Paris polyphylla plant diseases and insect pests image data set C Perform image feature matching and generate pest and disease type identification data of Paris polyphylla plants based on the image feature matching results ; when or and If the image feature matching is successful, it means that the Paris polyphylla plant has the kth Paris polyphylla plant disease and pest, then the Paris polyphylla plant disease and pest type identification data is output. If there are pests and diseases, the pest and disease type information of Paris polyphylla plants will be output; when or and If the image features are not matched successfully, it means that the Paris polyphylla plant has pests and diseases, then the Paris polyphylla plant pest and disease type identification data is output. If there are no pests and diseases, directly execute step S7.

[0030] When pests and diseases exist, the pest and disease control method analysis and processing of the pest and disease control method of the Paris polyphylla plant is performed according to the pest and disease type identification data of the Paris polyphylla plant and the pest and disease control method data of the Paris polyphylla plant. The operation steps for generating the pest and disease control method analysis data of the Paris polyphylla plant are as follows: S61. Identification data of pests and diseases of Paris polyphylla plants To establish a data set of pest control methods for Paris polyphylla plants when pests and diseases exist ,in The data of the pest control method of the Paris polyphylla plant corresponding to the k-th Paris polyphylla plant pest type, the Paris polyphylla plant pest control method data represents the optimal pest control treatment process and the control equipment information set according to the pest standards of different types of Paris polyphylla plants; S62. Identify the pest and disease types of Paris polyphylla plants Data collection on pest control methods for Paris polyphylla plants Data on pest control methods for Rhizoma Paridis Character matching of the pest and disease type of Paris polyphylla is performed and constructed into Paris polyphylla plant pest and disease control method analysis data D. The specific operation steps for generating Paris polyphylla plant pest and disease control method analysis data D are as follows: S621, initialization, update the maximum number of iterations T of the algorithm and randomly initialize the prevention and control method in the optimization space to identify the position of the osprey population. The position initialization formula is: ,in It indicates that the prevention and control method identifies the position of osprey i in the j-dimensional space, that is, the prevention and control method identifies the position of osprey i in the space dimension Data collection of pest control methods for Paris polyphylla plants The position in the search space, To find the optimal lower boundary, that is, the data set of pest control methods for Paris polyphylla The lower bound in the search space, To find the optimal upper boundary, that is, the data set of pest control methods for Paris polyphylla The upper boundary in the search space, r represents a random number in the interval [0,1]; S622, exploration stage, the exploration stage of the osprey population update is based on the simulation of the natural behavior of the osprey, and the control method identification osprey is in the data set of the pest control method of the Paris polyphylla plant. Randomly detected in the search space related to the identification data of pests and diseases of Paris polyphylla Matching data on pest and disease control methods for Paris polyphylla plants The target position is attacked, and the corresponding control method is updated to identify the new position of the osprey based on the simulated control method identification osprey moving towards the target. The control method identification osprey position update formula ,in It indicates the position of osprey i in the j-dimensional space after the prevention and control method is updated, that is, the spatial dimension of osprey i after the prevention and control method is updated is Data collection of pest control methods for Paris polyphylla plants Position in the search space; Represents the data set of prevention and control method identification of osprey in the prevention and control methods of diseases and insect pests of Paris polyphylla plants Search the search space to find the identification data of pests and diseases of Paris polyphylla Matching data on pest and disease control methods for Paris polyphylla plants The location of the target; represents a constant with a value of 1 or 2; if the updated new position is better, the initial position before the update of the prevention and control method to identify the osprey is replaced according to the exploration stage position replacement formula. The exploration stage position replacement formula is ,in It represents the optimal position of osprey i in the j-dimensional space identified by the prevention and control method in the exploration stage, that is, the spatial dimension of osprey i after the prevention and control method in the exploration stage is updated Data collection of pest control methods for Paris polyphylla plants Search the search space to find the identification data of pests and diseases of Paris polyphylla The most matching data on pest and disease control methods for Paris polyphylla location; express Data on pest and disease control methods for Paris polyphylla plants at locations Data on identification of pests and diseases of Paris polyphylla The fitness value of express Data on pest and disease control methods for Paris polyphylla plants at locations Data on identification of pests and diseases of the Paris polyphylla plant The fitness value of S623, Development stage, identification of control methods, Osprey in Paris polyphylla plant disease and insect pest control method data collection Hunting and eating and identification of pests and diseases of Paris polyphylla plants in search space Matching data on pest and disease control methods for Paris polyphylla plants Objective, the development phase of the algorithm control method identification osprey population update is based on modeling the simulation of the natural behavior of the control method identification osprey, calculating new random positions as suitable for edible and Paris polyphylla plant pest type identification data Matching data on pest and disease control methods for Paris polyphylla plants The location of the target, calculate the new edible and Paris polyphylla plant pest and disease type identification data Matching data on pest and disease control methods for Paris polyphylla plants The formula for the target's position ,in It means that the new random position of osprey i in the j-dimensional space after the control method is updated is the position suitable for edible fish, that is, the space dimension of osprey i after the control method is updated is Data collection of pest control methods for Paris polyphylla plants New random positions in the search space as suitable data for identifying pest and disease types of edible and Paris polyphylla plants Matching data on pest and disease control methods for Paris polyphylla plants The position of the target, t represents the number of iterations of the current algorithm; if the value of the objective function is improved at this new position, the initial position before the update of the prevention and control method to identify the osprey is replaced according to the development stage position replacement formula, and the development stage position replacement formula is ,in It represents the optimal position of osprey i in the j-dimensional space after the prevention and control method is identified in the development stage, that is, the spatial dimension of osprey i after the prevention and control method is updated in the development stage is Data collection of pest control methods for Paris polyphylla plants Search for the best position in the space; express Data on pest and disease control methods for Paris polyphylla plants at locations Data on identification of pests and diseases of Paris polyphylla The fitness value of S624: When the algorithm meets the maximum number of iterations, output the identification data of pest and disease types of Paris polyphylla plants The most matching data on pest and disease control methods for Paris polyphylla , otherwise continue to execute steps S622 to S623 until the maximum number of iterations is met; S625, the Paris polyphylla plant disease and insect pest control method data output in step S624 Through data identification, analysis data D of pest and disease control methods for Paris polyphylla plants was constructed.

[0031] Through the Paris polyphylla plant pest and disease type identification unit, the Paris polyphylla plant growth image information or the Paris polyphylla plant growth image preprocessing information is combined with the intelligent search algorithm and the Paris polyphylla plant pest and disease image information based on big data storage to scientifically and accurately identify the Paris polyphylla plant pest and disease type, thereby realizing intelligent and precise monitoring of pests and diseases in the Paris polyphylla planting process; the Paris polyphylla plant pest and disease control plan analysis unit accurately analyzes the Paris polyphylla plant pest and disease control methods based on the Paris polyphylla plant pest and disease type identification information combined with the intelligent recognition algorithm and the scientifically preset Paris polyphylla plant pest and disease control method information, thereby realizing scientific matching of the pest and disease control plan in the Paris polyphylla planting process, improving the efficiency and reliability of pest and disease control in the Paris polyphylla planting process, and realizing intelligent and digital management of Paris polyphylla planting operations.

[0032] For further information, see Figure 1 - Figure 2 The steps for constructing the Paris polyphylla planting pest and disease control analysis result data and performing the Paris polyphylla planting pest and disease control analysis result feedback operation are as follows: S71, generating the Paris polyphylla plant growth image data set A or the Paris polyphylla plant growth image preprocessing data set , Paris polyphylla plant disease and insect pest type identification data , Paris polyphylla plant disease and insect pest control method analysis data D are collected and combined, and Paris polyphylla planting disease and insect pest control analysis result data H is constructed, where ; S72, push the Paris polyphylla planting disease and insect pest control analysis result data H online to the Paris polyphylla planting disease and insect pest monitoring terminal through the Internet of Things communication network and perform the Paris polyphylla planting disease and insect pest control analysis result feedback output operation in conjunction with the display screen.

[0033] Through the Paris polyphylla planting pest and disease control analysis result construction unit, the Paris polyphylla planting pest and disease control analysis result information is efficiently and accurately constructed based on the Paris polyphylla plant growth image or preprocessed image, the Paris polyphylla plant pest and disease analysis results and the Paris polyphylla plant pest and disease control method analysis results, thereby realizing the accurate and standardized collection of the Paris polyphylla planting process pest and disease control monitoring information; the Paris polyphylla planting pest and disease control feedback unit pushes the Paris polyphylla planting pest and disease control analysis result information to the Paris polyphylla planting pest and disease monitoring terminal through the Internet of Things communication network in a timely and efficient manner and cooperates with the display screen for intuitive and clear feedback output, thereby improving the efficiency and quality of the pest and disease control monitoring during the Paris polyphylla planting process.

[0034] Example 2 Please refer to Figure 1 - Figure 2 , a Paris polyphylla planting pest control scheme data analysis system, used to implement a Paris polyphylla planting pest control scheme data analysis method, the system includes a Paris polyphylla planting pest image acquisition module, a Paris polyphylla planting pest analysis module, and a Paris polyphylla planting pest analysis result output module; The Paris polyphylla planting pest and disease image acquisition module includes a Paris polyphylla plant growth image acquisition unit, a Paris polyphylla plant surface attached occlusion image storage unit, a Paris polyphylla plant growth image occlusion type recognition unit, a Paris polyphylla plant growth image occlusion removal algorithm storage unit, a Paris polyphylla plant growth image occlusion removal algorithm matching unit, and a Paris polyphylla plant growth image occlusion preprocessing unit; The Paris polyphylla plant growth image acquisition unit is used to acquire Paris polyphylla plant growth image data through a shooting lens; the Paris polyphylla plant surface attached obstruction image storage unit is used to store the Paris polyphylla plant surface attached obstruction image data; the Paris polyphylla plant growth image obstruction type recognition unit is used to perform recognition processing on the type of obstruction attached to the surface of the Paris polyphylla plant in the Paris polyphylla plant growth image acquired based on the Paris polyphylla plant growth image data and the Paris polyphylla plant surface attached obstruction image data, and generate the Paris polyphylla plant growth image obstruction type recognition data; the Paris polyphylla plant growth image obstruction removal algorithm storage unit is used to store the Paris polyphylla plant growth image obstruction removal algorithm type data; a Paris polyphylla plant growth image occlusion removal algorithm matching unit, which performs a removal algorithm type matching process on occlusions attached to the plant surface in the Paris polyphylla plant growth image according to the Paris polyphylla plant growth image occlusion type recognition data and the Paris polyphylla plant growth image occlusion removal algorithm type data, and generates Paris polyphylla plant growth image occlusion removal algorithm analysis data; a Paris polyphylla plant growth image occlusion preprocessing unit, which performs a removal preprocessing process on occlusions attached to the plant surface in the Paris polyphylla plant growth image according to the Paris polyphylla plant growth image data and the Paris polyphylla plant growth image occlusion removal algorithm analysis data, and generates Paris polyphylla plant growth image preprocessing data; The Paris polyphylla planting pest and disease analysis module includes a Paris polyphylla plant pest and disease image storage unit, a Paris polyphylla plant pest and disease type identification unit, a Paris polyphylla plant pest and disease control method storage unit, and a Paris polyphylla plant pest and disease control plan analysis unit; A Paris polyphylla plant pest and disease image storage unit is used to store Paris polyphylla plant pest and disease image data; a Paris polyphylla plant pest and disease type identification unit is used to perform Paris polyphylla plant pest and disease type identification processing on Paris polyphylla plant growth image data or Paris polyphylla plant growth image preprocessing data and Paris polyphylla plant pest and disease image data, and generate Paris polyphylla plant pest and disease type identification data; a Paris polyphylla plant pest and disease prevention method storage unit is used to store Paris polyphylla plant pest and disease prevention method data; a Paris polyphylla plant pest and disease prevention scheme analysis unit is used to perform Paris polyphylla plant pest and disease prevention method analysis processing on Paris polyphylla plant pest and disease according to Paris polyphylla plant pest and disease type identification data and Paris polyphylla plant pest and disease prevention method data, and generate Paris polyphylla plant pest and disease prevention method analysis data; The Paris polyphylla planting pest and disease analysis result output module includes a Paris polyphylla planting pest and disease control analysis result construction unit and a Paris polyphylla planting pest and disease control feedback unit; The Paris polyphylla planting disease and pest control analysis result construction unit is used to construct the Paris polyphylla planting disease and pest control analysis result data; the Paris polyphylla planting disease and pest control feedback unit pushes the Paris polyphylla planting disease and pest control analysis result data to the Paris polyphylla planting disease and pest monitoring terminal online based on the Paris polyphylla planting disease and pest control analysis result data combined with the Internet of Things communication network and executes the Paris polyphylla planting disease and pest control analysis result feedback output operation through the display screen.

[0035] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data analysis method for Paris polyphylla planting pest control program, characterized in that: The method comprises the following steps: S1. Collecting Paris polyphylla plant growth image data; S2, performing identification processing on the type of obstructions attached to the plant surface in the Paris polyphylla plant growth image collected according to the Paris polyphylla plant growth image data and the Paris polyphylla plant surface obstruction image data, generating Paris polyphylla plant growth image obstruction type identification data, and directly executing step S5 when there is no obstruction; S3, when there is an obstruction, performing a matching process of an algorithm type for eliminating obstructions attached to the plant surface in the Paris polyphylla plant growth image according to the Paris polyphylla plant growth image obstruction type identification data and the Paris polyphylla plant growth image obstruction elimination algorithm type data, and generating Paris polyphylla plant growth image obstruction elimination algorithm analysis data; S4, performing preprocessing of removing obstructions attached to the plant surface in the Paris polyphylla plant growth image according to the Paris polyphylla plant growth image data and the Paris polyphylla plant growth image obstruction removal algorithm analysis data, and generating Paris polyphylla plant growth image preprocessing data; S5, performing identification processing of the pest and disease type of the Paris polyphylla plant on the Paris polyphylla plant growth image data or the Paris polyphylla plant growth image preprocessing data and the Paris polyphylla plant pest and disease image data to generate Paris polyphylla plant pest and disease type identification data, and directly executing step S7 when there is no pest and disease; S6. When pests and diseases exist, analyzing and processing the pest and disease control methods of the Paris polyphylla plants according to the Paris polyphylla plant pest and disease type identification data and the Paris polyphylla plant pest and disease control method data to generate Paris polyphylla plant pest and disease control method analysis data; S7. Construct the data of the analysis results of the pest and disease control in Paris polyphylla cultivation and perform feedback work on the analysis results of the pest and disease control in Paris polyphylla cultivation.

2. The method for analyzing data of a Paris polyphylla planting pest control program according to claim 1, characterized in that: The S1 comprises the following steps: S11, collecting the growth image information of the Paris polyphylla plants in the natural state in the Paris polyphylla planting field online through a shooting lens, and generating a Paris polyphylla plant growth image data set ;in represents the yth collected image data of Paris polyphylla plant growth, Indicates the maximum number of Paris polyphylla plant growth images.

3. The method for analyzing data of a Paris polyphylla planting pest control program according to claim 2, characterized in that: The S2 comprises the following steps: S21. Establishing a data set of images of obstructions attached to the surface of Paris polyphylla plants , ;in represents the image data of the obstruction attached to the surface of the Paris polyphylla plant corresponding to the oth Paris polyphylla plant obstruction type, Indicates the maximum number of types of shades of Paris polyphylla plants; S22, using the KD tree nearest neighbor search algorithm to As described in B Perform image feature matching and generate Paris polyphylla plant growth image occlusion type recognition data based on the image feature matching results ; when and If the image feature matching is successful, the occlusion type identification data of the Paris polyphylla plant growth image will be output. If there is an obstruction, the type of obstruction information of the Paris polyphylla plant is output; when and If the image features are not matched successfully, the Paris polyphylla plant growth image occlusion type recognition data is output If there is no obstruction, then directly execute step S5.

4. The method for analyzing data of pest control schemes for Paris polyphylla according to claim 3, characterized in that: The S3 comprises the following steps: S31, said When there are occluders, establish a data set of occluder removal algorithm types for Paris polyphylla plant growth images ,in Represents the type data of the occluder removal algorithm of the Paris polyphylla plant growth image corresponding to the o-th Paris polyphylla plant occluder type; S32, using the BERT language model algorithm to With the As stated in Perform keyword matching of Paris polyphylla plant shielding type and search for the Corresponding Paris polyphylla plant growth image occlusion removal algorithm type data , and generate the occlusion removal algorithm analysis data of Paris polyphylla plant growth image through data identification .

5. The method for analyzing data of pest control schemes for Paris polyphylla according to claim 4, characterized in that: The S4 comprises the following steps: S41, adopting the The corresponding image occlusion removal algorithm is described in A According to the Paris polyphylla plant growth image number, the Paris polyphylla plant growth image is sequentially preprocessed to remove the obstructions attached to the plant surface, and a Paris polyphylla plant growth image preprocessing data set is generated. ,in Represents the preprocessing data of the y-th Paris polyphylla plant growth image.

6. The method for analyzing data of a Paris polyphylla planting pest control program according to claim 5, characterized in that: The S5 comprises the following steps: S51. Establishing a data set of plant diseases and insect pests images of Paris polyphylla ;in represents the image data of Paris polyphylla plant diseases and insect pests corresponding to the kth Paris polyphylla plant disease and insect pest type, It represents the maximum value of the number of pests and diseases types of Paris polyphylla plants; S52, using a unified cost search algorithm to generate the or As stated in As described in C Perform image feature matching and generate identification data of pests and diseases of Paris polyphylla plants based on the image feature matching results ; when or and If the image feature matching is successful, the identification data of pests and diseases of Paris polyphylla plants will be output. If there are pests and diseases, the pest and disease type information of Paris polyphylla plants will be output; when or and If the image features are not matched successfully, the identification data of pests and diseases of Paris polyphylla plants will be output. If there are no pests and diseases, directly execute step S7.

7. The method for analyzing data of pest control schemes for Paris polyphylla according to claim 6, characterized in that: The S6 comprises the following steps: S61, when the To establish a data set of pest control methods for Paris polyphylla plants when pests and diseases exist ,in Represents the data of the prevention and control methods of Paris polyphylla plant diseases and pests corresponding to the kth Paris polyphylla plant disease and pest type; S62, identifying the pest and disease type data of the Paris polyphylla plant Data set on pest control methods for Paris polyphylla plants Data on pest control methods for Paris polyphylla plants Character matching of the pest and disease type of Paris polyphylla is performed, and the Paris polyphylla plant pest and disease control method analysis data D is constructed. The specific operation steps for generating the Paris polyphylla plant pest and disease control method analysis data D are as follows: S621, initialization, updating the maximum number of iterations T of the algorithm and randomly initializing the prevention and control method in the optimization space to identify the location of the osprey population; S622, exploration stage, the exploration stage of updating the population of ospreys is based on the simulation of the natural behavior of ospreys, and the ospreys are identified in the data set of pest control methods of the Paris polyphylla plants. Randomly detected in the search space the identification data of pests and diseases types of the Paris polyphylla plants Matching data on pest control methods for Paris polyphylla plants The target position is attacked, and the new position of the osprey identified by the corresponding control method is updated on the basis of simulating the movement of the osprey toward the target; if the updated new position is better, the initial position of the osprey identified by the control method is replaced before the update according to the position replacement formula in the exploration stage; S623, development stage, prevention and control method identification, osprey in the Paris polyphylla plant disease and insect pest prevention method data set Hunting and eating pests and diseases type identification data of the Paris polyphylla plant in the search space Matching data on pest control methods for Paris polyphylla plants Objective, the development phase of the algorithm control method identification osprey population update is based on modeling the simulation of the natural behavior of the control method identification osprey, calculating new random positions as suitable for edible and the identification data of the pest type of the Paris polyphylla plant Matching data on pest control methods for Paris polyphylla plants Target location, calculation of new suitable food and identification data of pest and disease types of the Paris polyphylla plant Matching data on pest control methods for Paris polyphylla plants the position of the target; if the value of the target function is improved at this new position, the initial position before the update of the control method identification osprey is replaced according to the position replacement formula in the development stage; S624: When the algorithm meets the maximum number of iterations, output the identification data of the pest and disease type of the Paris polyphylla plant. The most matching data on pest control methods for the Paris polyphylla plant , otherwise continue to execute steps S622 to S623 until the maximum number of iterations is met; S625, the Paris polyphylla plant disease and insect pest control method data output in step S624 After data identification, analysis data D of pest and disease control methods for Paris polyphylla plants is constructed.

8. The method for analyzing data of pest control schemes for Paris polyphylla according to claim 7, characterized in that: The S7 comprises the following steps: S71, the generated A or the , , the D collects and combines data, and constructs the Paris polyphylla planting pest and disease control analysis result data H; S72, pushing the H online to the Paris polyphylla planting pest and disease monitoring terminal through the Internet of Things communication network and executing the Paris polyphylla planting pest and disease control analysis result feedback output operation in conjunction with the display screen.

9. A data analysis system for Paris polyphylla planting pest control scheme, used to implement a data analysis method for Paris polyphylla planting pest control scheme according to any one of claims 1 to 8, characterized in that: The system comprises a Paris polyphylla planting disease and insect pest image acquisition module, a Paris polyphylla planting disease and insect pest analysis module, and a Paris polyphylla planting disease and insect pest analysis result output module.

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