A data analysis system and method for preventing and controlling diseases and pests in Paris vietnamensis cultivation

Abstract

The present invention relates to the technical field of data processing for pests and diseases of Paris vietnamensis, and discloses a data analysis system and method for preventing and controlling pests and diseases in Paris vietnamensis cultivation. By combining the identification information of the types of occluders in the growth images of Paris vietnamensis plants with the intelligent search algorithm and the type information of the preset standard occluder removal algorithm for the growth images of Paris vietnamensis plants, a precise analysis of the type of occluder feature removal algorithm in the growth images of Paris vietnamensis plants is carried out, so as to realize the scientific matching of the type of occluder interference features in the pest and disease monitoring images of Paris vietnamensis with the occluder removal algorithm object, and improve the quality of the acquisition of pest and disease monitoring images of Paris vietnamensis; independently and efficiently execute the preprocessing of removing the interference features of the occluders attached to the plant surface in the growth images of Paris vietnamensis plants, realize the efficient and precise processing of the occluder interference features in the pest and disease monitoring images of Paris vietnamensis, improve the authenticity of the acquisition of pest and disease monitoring images of Paris vietnamensis, and improve the prevention and control effect of pests and diseases in Paris vietnamensis cultivation.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing for pests and diseases of Paris vietnamensis (Takht.) H. Li, and specifically relates to a data analysis system and method for pest control solutions in Paris vietnamensis (Takht.) H. Li planting. Background Art

[0002] Paris vietnamensis (Takht.) H. Li is a general term for plants of the genus Paris in the family Trilliaceae and is a perennial herb. During the growth process of Paris vietnamensis (Takht.) H. Li planting, it is necessary to regularly monitor the pests and diseases occurring in the planting fields; in the existing pest and disease supervision operations for Paris vietnamensis (Takht.) H. Li planting, images of the growth status of Paris vietnamensis (Takht.) H. Li are directly collected for pest and disease analysis of Paris vietnamensis (Takht.) H. Li planting. Since there are easily attachment of moisture, soil particles, dust, dead leaves and other obstacles on the surface of Paris vietnamensis (Takht.) H. Li plants in the natural environment, it is easy to cause distortion of the collected images of the growth status of Paris vietnamensis (Takht.) H. Li, resulting in inaccurate pest and disease analysis results for Paris vietnamensis (Takht.) H. Li planting. At the same time, it is also impossible to accurately analyze the pest control solutions for Paris vietnamensis (Takht.) H. Li planting, reducing the quality and efficiency of pest control operations for Paris vietnamensis (Takht.) H. Li planting. Summary of the Invention

[0003] (I) Technical Problems to be Solved

[0004] To solve the above problems in the existing pest and disease supervision operations for Paris vietnamensis (Takht.) H. Li planting, where images of the growth status of Paris vietnamensis (Takht.) H. Li are directly collected for pest and disease analysis of Paris vietnamensis (Takht.) H. Li planting. Since there are easily attachment of moisture, soil particles, dust, dead leaves and other obstacles on the surface of Paris vietnamensis (Takht.) H. Li plants in the natural environment, it is easy to cause distortion of the collected images of the growth status of Paris vietnamensis (Takht.) H. Li, resulting in inaccurate pest and disease analysis results for Paris vietnamensis (Takht.) H. Li planting. At the same time, it is also impossible to accurately analyze the pest control solutions for Paris vietnamensis (Takht.) H. Li planting, reducing the quality and efficiency of pest control operations for Paris vietnamensis (Takht.) H. Li planting. The purpose is to accurately collect the growth image information of Paris vietnamensis (Takht.) H. Li plants, intelligently identify the types of obstacles on the growth images of Paris vietnamensis (Takht.) H. Li plants, scientifically match the obstacle removal algorithm objects for the growth images of Paris vietnamensis (Takht.) H. Li plants, accurately generate the preprocessing information of the growth images of Paris vietnamensis (Takht.) H. Li plants, scientifically identify the types of pests and diseases of Paris vietnamensis (Takht.) H. Li plants, accurately analyze the pest control methods for Paris vietnamensis (Takht.) H. Li plants, and visually and visually output the pest control analysis result information for Paris vietnamensis (Takht.) H. Li planting.

[0005] (II) Technical Solutions

[0006] The present invention is realized through the following technical solutions: A data analysis method for pest control solutions in Paris vietnamensis (Takht.) H. Li planting, the method comprising the following steps:

[0007] S1. Collect the growth image data of Paris vietnamensis (Takht.) H. Li plants;

[0008] S2. Based on the growth image data of Paris vietnamensis (Takht.) H. Li plants and the image data of the obstacles attached to the surface of Paris vietnamensis (Takht.) H. Li plants, perform identification processing on the types of obstacles attached to the surface of the plants in the collected growth images of Paris vietnamensis (Takht.) H. Li plants to generate identification data on the types of obstacles on the growth images of Paris vietnamensis (Takht.) H. Li plants. When there are no obstacles, directly execute step S5;

[0009] S3. When there is an occluder, perform matching processing on the occluder removal algorithm type for the occluder on the surface of the Paris vietnamensis plant in the Paris vietnamensis plant growth image according to the occluder type recognition data and the occluder removal algorithm type data of the Paris vietnamensis plant growth image, and generate the occluder removal algorithm analysis data of the Paris vietnamensis plant growth image;

[0010] S4. Perform preprocessing on the occluder attached to the surface of the plant in the Paris vietnamensis plant growth image based on the Paris vietnamensis plant growth image data and the occluder removal algorithm analysis data of the Paris vietnamensis plant growth image, and generate the preprocessing data of the Paris vietnamensis plant growth image;

[0011] S5. Perform pest and disease type recognition processing on the Paris vietnamensis plant by using the Paris vietnamensis plant growth image data or the preprocessing data of the Paris vietnamensis plant growth image and the pest and disease image data of the Paris vietnamensis plant, and generate the pest and disease type recognition data of the Paris vietnamensis plant. When there are no pests and diseases, directly execute step S7;

[0012] S6. When there are pests and diseases, perform analysis processing on the pest and disease control methods of the Paris vietnamensis plant according to the pest and disease type recognition data and the pest and disease control method data of the Paris vietnamensis plant, and generate the pest and disease control method analysis data of the Paris vietnamensis plant;

[0013] S7. Construct the pest and disease control analysis result data of Paris vietnamensis planting and perform the feedback operation of the pest and disease control analysis result of Paris vietnamensis planting.

[0014] Preferably, the operation steps for collecting the Paris vietnamensis plant growth image data are as follows:

[0015] S11. Online collect the Paris vietnamensis plant growth image information in the natural state in the Paris vietnamensis planting field through the shooting lens, and generate the Paris vietnamensis plant growth image data set ; where represents the y-th Paris vietnamensis plant growth image data collected, represents the maximum value of the number of Paris vietnamensis plant growth images.

[0016] The present invention accurately and efficiently collects the Paris vietnamensis plant growth image information through the shooting lens, achieving the effect of providing reliable data support for accurately analyzing the pest and disease information of the Paris vietnamensis plant subsequently.

[0017] Preferably, perform occluder type recognition processing on the occluder attached to the surface of the plant in the Paris vietnamensis plant growth image collected according to the Paris vietnamensis plant growth image data and the occluder image data attached to the surface of the Paris vietnamensis plant, and generate the occluder type recognition data of the Paris vietnamensis plant growth image. When there is no occluder, the operation steps for directly executing step S5 are as follows:

[0018] S21. Establish the occluder image data set attached to the surface of the Paris vietnamensis plant , ; wherein represents the image data of the occluder attached to the surface of the Paris vietnamensis plant corresponding to the o-th type of Paris vietnamensis plant occluder, represents the maximum value of the number of Paris vietnamensis plant occluder types, and the Paris vietnamensis plant occluder types include water droplet occluders, soil particle occluders, dust occluders, and withered leaf occluders; the image data of the occluder attached to the surface of the Paris vietnamensis plant represents the real-scene feature image data of different types of occluders attached to the surface of the Paris vietnamensis plant in the natural state;

[0019] S22. Use the K-D tree nearest neighbor search algorithm to match the Paris vietnamensis plant growth image data in the Paris vietnamensis plant growth image data set A with the Paris vietnamensis plant surface-attached occluder image data in the Paris vietnamensis plant surface-attached occluder image data set B for image feature matching, and generate Paris vietnamensis plant growth image occluder type recognition data ;

[0020] When and the image feature matching is successful, indicating that the o-th type of Paris vietnamensis plant occluder is attached to the surface of the Paris vietnamensis plant, then output the Paris vietnamensis plant growth image occluder type recognition data as having an occluder, and output the Paris vietnamensis plant occluder type information;

[0021] When and the image features are not successfully matched, indicating that there is no occluder on the surface of the Paris vietnamensis plant, then output the Paris vietnamensis plant growth image occluder type recognition data as having no occluder, and directly execute step S5 at this time.

[0022] The present invention intelligently analyzes the occluder type in the Paris vietnamensis plant growth image by combining the Paris vietnamensis plant growth image information with the K-D tree nearest neighbor search algorithm and the scientifically stored Paris vietnamensis plant surface-attached occluder image information, achieving the effect of scientifically identifying the occluder interference features in the Paris vietnamensis plant pest and disease monitoring image.

[0023] Preferably, when there is an occluder, the operation steps of performing the matching process of the occluder removal algorithm type for the occluder attached to the surface of the plant in the Paris vietnamensis plant growth image according to the Paris vietnamensis plant growth image occluder type recognition data and the Paris vietnamensis plant growth image occluder removal algorithm type data are as follows:

[0024] S31. The Paris vietnamensis plant growth image occluder type recognition data When there are occluders, establish a data set of occluder removal algorithm types for the growth images of Paris vietnamensis plants , where represents the data of the occluder removal algorithm type for the growth image of Paris vietnamensis plants corresponding to the o-th type of occluder of Paris vietnamensis plants; the data of the occluder removal algorithm type for the growth image of Paris vietnamensis plants represents the optimal image occluder removal algorithm type information set for different types of occluders attached to the surface of Paris vietnamensis plants; the types of image occluder removal algorithms include the rasterization-based occlusion culling algorithm, the BSP tree culling algorithm, the PVS culling algorithm, and the ray tracing culling algorithm;

[0025] S32. Use the BERT language model algorithm to match the occluder type recognition data of the growth image of Paris vietnamensis plants with the data set of occluder removal algorithm types for the growth image of Paris vietnamensis plants in the data of the occluder removal algorithm type for the growth image of Paris vietnamensis plants to perform keyword matching for the occluder type of Paris vietnamensis plants, search for the data of the occluder removal algorithm type corresponding to the occluder type recognition data of the growth image of Paris vietnamensis plants , and generate the occluder removal algorithm analysis data for the growth image of Paris vietnamensis plants through data identification . .

[0026] The present invention accurately analyzes the occluder feature removal algorithm type in the growth image of Paris vietnamensis plants by combining the occluder type recognition information of the growth image of Paris vietnamensis plants with the BERT language model algorithm and the standard preset occluder removal algorithm type information for the growth image of Paris vietnamensis plants, achieving the effect of scientifically matching the image occluder removal algorithm object based on the occluder interference feature type in the Paris vietnamensis plant disease and pest monitoring image.

[0027] Preferably, the operation steps of performing preprocessing for removing the occluders attached to the surface of the plants in the growth image of Paris vietnamensis plants based on the growth image data of Paris vietnamensis plants and the occluder removal algorithm analysis data for the growth image of Paris vietnamensis plants, and generating the preprocessing data for the growth image of Paris vietnamensis plants are as follows:

[0028] S41. Use the image occluder removal algorithm corresponding to the occluder removal algorithm analysis data for the growth image of Paris vietnamensis plants to perform preprocessing for removing the occluders attached to the surface of the plants in the growth image of Paris vietnamensis plants in the growth image data set A of Paris vietnamensis plants in an orderly manner according to the growth image number of Paris vietnamensis plants, and generate a preprocessing data set for the growth image of Paris vietnamensis plants , where represents the preprocessing data for the y-th growth image of Paris vietnamensis plants.

[0029] The present invention pre - processes the interference features of the obstacles attached to the surface of the Paris vietnamensis plants in the growth images of Paris vietnamensis plants by autonomous and efficient execution, achieving the effect of efficiently and accurately processing the interference features of the obstacles in the Paris vietnamensis plant disease and pest monitoring images.

[0030] Preferably, the Paris vietnamensis plant growth image data or the pre - processed Paris vietnamensis plant growth image data is subjected to the identification process of the Paris vietnamensis plant disease and pest types together with the Paris vietnamensis plant disease and pest image data to generate the Paris vietnamensis plant disease and pest type identification data. When there are no diseases and pests, the operation steps of directly executing step S7 are as follows:

[0031] S51. Establish a set of Paris vietnamensis plant disease and pest image data ; where represents the Paris vietnamensis plant disease and pest image data corresponding to the k - th Paris vietnamensis plant disease and pest type, represents the maximum value of the number of Paris vietnamensis plant disease and pest types. The Paris vietnamensis plant disease and pest types include the disease and pest types of scarab larvae, cutworms, wireworms, leaf miners, damping - off disease, stem rot disease, leaf spot disease, and brown spot disease; the Paris vietnamensis plant disease and pest image data represents the standard image data corresponding to the phenotypic symptoms of different types of Paris vietnamensis plant diseases and pests;

[0032] S52. Use the uniform - cost search algorithm to match the Paris vietnamensis plant growth image data in the generated set A of Paris vietnamensis plant growth image data or the pre - processed Paris vietnamensis plant growth image data in the pre - processed Paris vietnamensis plant growth image data set with the Paris vietnamensis plant disease and pest image data in the set C of Paris vietnamensis plant disease and pest image data for image feature matching, and generate the Paris vietnamensis plant disease and pest type identification data ;

[0033] When or and fail to match successfully in image features, indicating that there are diseases and pests in the Paris vietnamensis plants, then output the Paris vietnamensis plant disease and pest type identification data as no diseases and pests, and directly execute step S7 at this time.

[0034] The present invention scientifically and accurately identifies the Paris vietnamensis plant disease and pest types by combining the Paris vietnamensis plant growth image information or the pre - processed Paris vietnamensis plant growth image information with the uniform - cost search algorithm and the Paris vietnamensis plant disease and pest image information based on big data storage, achieving the effect of intelligent and accurate monitoring of diseases and pests in the Paris vietnamensis planting process.

[0035] 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:

[0036] 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;

[0037] 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:

[0038] 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];

[0039] 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 Locate the target and attack. On the basis of simulating the prevention and control method to identify the movement of the osprey towards the target, update the corresponding prevention and control method to identify the new position of the osprey. The formula for updating the position of the prevention and control method to identify the osprey , where represents the position of the osprey i updated by the prevention and control method in the j-dimensional space, that is, after the osprey i is updated by the prevention and control method, in the space dimension of the data set of the prevention and control method for diseases and pests of Paris vietnamensis plants The position in the search space; represents the osprey identified by the prevention and control method in the data set of the prevention and control method for diseases and pests of Paris vietnamensis plants Search in the search space for the data of the prevention and control method for diseases and pests of Paris vietnamensis plants that matches the data for identifying the type of diseases and pests of Paris vietnamensis plants The position of the target; represents a constant with a value of 1 or 2; if the updated new position is better, replace the initial position before the update of the osprey identified by the prevention and control method according to the position replacement formula in the exploration stage. The position replacement formula in the exploration stage is , where represents the optimal position of the osprey i identified by the prevention and control method in the j-dimensional space in the exploration stage, that is, after the osprey i is updated by the prevention and control method in the exploration stage, in the space dimension of the data set of the prevention and control method for diseases and pests of Paris vietnamensis plants Search in the search space for the data of the prevention and control method for diseases and pests of Paris vietnamensis plants that most matches the data for identifying the type of diseases and pests of Paris vietnamensis plants The position; represents The data of the prevention and control method for diseases and pests of Paris vietnamensis plants at the position and the data for identifying the type of diseases and pests of Paris vietnamensis plants The fitness value, represents The data of the prevention and control method for diseases and pests of Paris vietnamensis plants at the position and the data

[0040] S623. In the development stage, the osprey identified by the prevention and control method hunts and eats in the data set of the prevention and control method for diseases and pests of Paris vietnamensis plants Search in the search space for the data of the prevention and control method for diseases and pests of Paris vietnamensis plants that matches the data for identifying the type of diseases and pests of Paris vietnamensis plants The development stage of the algorithm for preventing and controlling osprey population update by identifying the target is modeled based on the simulation of the natural behavior of ospreys for preventing and controlling, and a new random position is calculated as a suitable position for eating, which is related to the data for identifying the types of diseases and pests of the Paris vietnamensis plants The data for preventing and controlling diseases and pests of the Paris vietnamensis plants that matches The position of the target, and calculate a new suitable position for eating, which is related to the data for identifying the types of diseases and pests of the Paris vietnamensis plants The data for preventing and controlling diseases and pests of the Paris vietnamensis plants that matches The formula for the position of the target , where represents the new random position of osprey i for preventing and controlling in the j-dimensional space as the position suitable for eating fish, that is, after the update of osprey i for preventing and controlling, in the space dimension of The set of data for preventing and controlling diseases and pests of the Paris vietnamensis plants A new random position in the search space as a suitable position for eating, which is related to the data for identifying the types of diseases and pests of the Paris vietnamensis plants The data for preventing and controlling diseases and pests of the Paris vietnamensis plants that matches The position of the target, and t represents the current iteration number of the algorithm; if the value of the objective function is improved at this new position, replace the initial position before the update of the osprey for preventing and controlling according to the position replacement formula in the development stage. The position replacement formula in the development stage is , where represents the optimal position of osprey i for preventing and controlling in the j-dimensional space in the development stage, that is, after the update of osprey i for preventing and controlling in the development stage, in the space dimension of The set of data for preventing and controlling diseases and pests of the Paris vietnamensis plants The optimal position in the search space; represents The data for preventing and controlling diseases and pests of the Paris vietnamensis plants at the position, which is related to the data for identifying the types of diseases and pests of the Paris vietnamensis plants The fitness value;

[0041] S624. When the algorithm reaches the maximum number of iterations, output the data for preventing and controlling diseases and pests of the Paris vietnamensis plants that best matches the data for identifying the types of diseases and pests of the Paris vietnamensis plants , otherwise continue to execute steps S622 to S623 until the maximum number of iterations is reached;

[0042] S625. Construct the data for preventing and controlling diseases and pests of the Paris vietnamensis plants output in step S624 into the analysis data for preventing and controlling diseases and pests of the Paris vietnamensis plants through data identification.

[0043] ​​The present invention accurately analyzes the pest control method of Paris vietnamensis plants by combining the pest type recognition information of Paris vietnamensis plants with the osprey optimization algorithm and the scientifically preset pest control method information of Paris vietnamensis plants, so as to achieve the effect of scientifically matching the pest control plan in the process of Paris vietnamensis planting.

[0044] Preferably, the operation steps of constructing the pest control analysis result data of Paris vietnamensis planting and performing the feedback operation of the pest control analysis result of Paris vietnamensis planting are as follows:

[0045] S71. Collect and combine the generated set A of Paris vietnamensis plant growth image data or the preprocessed data set of Paris vietnamensis plant growth images , the pest type recognition data of Paris vietnamensis plants , the pest control method analysis data D of Paris vietnamensis plants to construct the pest control analysis result data H of Paris vietnamensis planting, where ;

[0046] S72. Online push the pest control analysis result data H of Paris vietnamensis planting to the pest monitoring end of Paris vietnamensis planting through the Internet of Things communication network and perform the feedback output operation of the pest control analysis result of Paris vietnamensis planting in combination with the display screen.

[0047] The present invention efficiently and accurately constructs the pest control analysis result information of Paris vietnamensis planting, and timely and efficiently pushes the pest control analysis result information of Paris vietnamensis planting to the pest monitoring end of Paris vietnamensis planting through the Internet of Things communication network and cooperates with the display screen for intuitive and clear feedback output, so as to achieve the effect of accurately standardizing the collection of pest control monitoring information in the process of Paris vietnamensis planting and visually outputting the pest control monitoring information in the process of Paris vietnamensis planting.

[0048] A data analysis system for the pest control plan of Paris vietnamensis planting, which is used to implement the data analysis method for the pest control plan of Paris vietnamensis planting. The system includes a pest image collection module for Paris vietnamensis planting, a pest analysis module for Paris vietnamensis planting, and a pest analysis result output module for Paris vietnamensis planting;

[0049] The pest image collection module for Paris vietnamensis planting includes a Paris vietnamensis plant growth image collection unit, a Paris vietnamensis plant surface attachment and occlusion image storage unit, a Paris vietnamensis plant growth image occlusion type recognition unit, a Paris vietnamensis plant growth image occlusion removal algorithm storage unit, a Paris vietnamensis plant growth image occlusion removal algorithm matching unit, and a Paris vietnamensis plant growth image occlusion preprocessing unit;

[0050] The Paris polyphylla plant growth image acquisition unit collects Paris polyphylla plant growth image data through a shooting lens; the Paris polyphylla plant surface attached obstacle image storage unit is used to store Paris polyphylla plant surface attached obstacle image data; the Paris polyphylla plant growth image obstacle type recognition unit performs recognition processing on the type of obstacles attached to the plant surface in the collected Paris polyphylla plant growth image based on the Paris polyphylla plant growth image data and the Paris polyphylla plant surface attached obstacle image data, and generates Paris polyphylla plant growth image obstacle type recognition data; the Paris polyphylla plant growth image obstacle removal algorithm storage unit is used to store Paris polyphylla plant growth image obstacle removal algorithm type data; the Paris polyphylla plant growth image obstacle removal algorithm matching unit performs matching processing on the type of obstacle removal algorithms for the obstacles attached to the plant surface in the Paris polyphylla plant growth image according to the Paris polyphylla plant growth image obstacle type recognition data and the Paris polyphylla plant growth image obstacle removal algorithm type data, and generates Paris polyphylla plant growth image obstacle removal algorithm analysis data; the Paris polyphylla plant growth image obstacle preprocessing unit performs preprocessing on the removal of obstacles 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 obstacle removal algorithm analysis data, and generates Paris polyphylla plant growth image preprocessing data;

[0051] 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 recognition unit, a Paris polyphylla plant pest and disease control method storage unit, and a Paris polyphylla plant pest and disease control plan analysis unit;

[0052] 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 recognition unit performs pest and disease type recognition processing on the Paris polyphylla plant by comparing the Paris polyphylla plant growth image data or the Paris polyphylla plant growth image preprocessing data with the Paris polyphylla plant pest and disease image data, and generates Paris polyphylla plant pest and disease type recognition 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 plan analysis unit performs analysis processing on the pest and disease control methods of the Paris polyphylla plant according to the Paris polyphylla plant pest and disease type recognition data and the Paris polyphylla plant pest and disease control method data, and generates Paris polyphylla plant pest and disease control method analysis data;

[0053] 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;

[0054] The Paris polyphylla Smith planting pest control analysis result construction unit is used to construct the Paris polyphylla Smith planting pest control analysis result data; the Paris polyphylla Smith planting pest control feedback unit pushes the Paris polyphylla Smith planting pest control analysis result data to the Paris polyphylla Smith planting pest monitoring end online based on the Internet of Things communication network and performs the Paris polyphylla Smith planting pest control analysis result feedback output operation through a display screen.

[0055] (III) Beneficial effects

[0056] The present invention provides a data analysis system and method for a Paris polyphylla Smith planting pest control plan. The following beneficial effects are achieved:

[0057] 1. The growth image information of Paris polyphylla Smith plants is accurately and efficiently collected through a shooting lens, providing reliable data support for accurately analyzing the pest and disease information of Paris polyphylla Smith plants in the subsequent process; based on the growth image information of Paris polyphylla Smith plants, combined with an intelligent search algorithm and the image information of the attached and occluding objects on the surface of Paris polyphylla Smith plants stored scientifically, the intelligent analysis of the occluding object type in the growth image of Paris polyphylla Smith plants is carried out, realizing the scientific identification of the occluding object interference characteristics in the Paris polyphylla Smith planting pest and disease monitoring image and improving the accuracy of Paris polyphylla Smith planting pest and disease identification; according to the occluding object type identification information in the growth image of Paris polyphylla Smith plants, combined with an intelligent search algorithm and the information of the occluding object removal algorithm type preset for the growth image of Paris polyphylla Smith plants, the accurate analysis of the occluding object feature removal algorithm type in the growth image of Paris polyphylla Smith plants is carried out, realizing the scientific matching of the occluding object removal algorithm object based on the occluding object interference feature type in the Paris polyphylla Smith planting pest and disease monitoring image and improving the quality of the Paris polyphylla Smith planting pest and disease monitoring image collection; the preprocessing of removing the interference characteristics of the attached and occluding objects on the surface of the plants in the growth image of Paris polyphylla Smith plants is autonomously and efficiently executed, realizing the efficient and accurate processing of the occluding object interference characteristics in the Paris polyphylla Smith planting pest and disease monitoring image, improving the authenticity of the Paris polyphylla Smith planting pest and disease monitoring image collection, and improving the Paris polyphylla Smith planting pest and disease control effect;

[0058] 2. By combining the growth image information of Paris polyphylla Smith plants or the preprocessing information of the growth image of Paris polyphylla Smith plants with an intelligent search algorithm and the pest and disease image information of Paris polyphylla Smith plants stored based on big data, the scientific and accurate identification of the pest and disease types of Paris polyphylla Smith plants is realized, and the intelligent and accurate monitoring of pests and diseases in the Paris polyphylla Smith planting process is achieved; according to the pest and disease type identification information of Paris polyphylla Smith plants, combined with an intelligent identification algorithm and the scientifically preset pest and disease control method information of Paris polyphylla Smith plants, the accurate analysis of the pest and disease control method of Paris polyphylla Smith plants is carried out, realizing the scientific matching of the pest and disease control plan in the Paris polyphylla Smith planting process, improving the efficiency and reliability of pest and disease control in the Paris polyphylla Smith planting process, and realizing the intelligent and digital management of the Paris polyphylla Smith planting operation;

[0059] III. By efficiently and accurately constructing the analysis result information of pest control for Paris vietnamensis planting based on the growth images or preprocessed images of Paris vietnamensis plants, the analysis results of pest and disease analysis of Paris vietnamensis plants, and the analysis results of pest control methods for Paris vietnamensis plants, the precise and standardized collection of pest control monitoring information during the Paris vietnamensis planting process is realized; the analysis result information of pest control for Paris vietnamensis planting is promptly and efficiently pushed to the pest and disease monitoring terminal of Paris vietnamensis planting through the Internet of Things communication network and visually and clearly fed back and output in cooperation with the display screen, improving the efficiency and quality of pest control monitoring during the Paris vietnamensis planting process. Brief Description of the Drawings

[0060] Figure 1 It is a schematic diagram of the modules of a data analysis system for a pest control plan for Paris vietnamensis planting provided by the present invention;

[0061] Figure 2 It is a flowchart of a data analysis method for a pest control plan for Paris vietnamensis planting provided by the present invention. Detailed Embodiments

[0062] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0063] Embodiments of the data analysis system and method for a pest control plan for Paris vietnamensis planting are as follows:

[0064] Example 1 Please refer to Figure 1 - Figure 2 , a data analysis method for a pest control plan for Paris vietnamensis planting, the method includes the following steps:

[0065] S1. Collect the growth image data of Paris vietnamensis plants;

[0066] S2. Perform the recognition processing of the types of attached and obstructive substances on the surface of the plants in the growth images of Paris vietnamensis plants collected based on the growth image data of Paris vietnamensis plants and the image data of attached and obstructive substances on the surface of Paris vietnamensis plants, generate the recognition data of the types of obstructive substances on the surface of the growth images of Paris vietnamensis plants. When there are no obstructive substances, directly execute step S5;

[0067] S3. When there are obstructive substances, perform the matching processing of the removal algorithm types of the attached and obstructive substances on the surface of the plants in the growth images of Paris vietnamensis plants according to the recognition data of the types of obstructive substances on the surface of the growth images of Paris vietnamensis plants and the data of the removal algorithm types of the attached and obstructive substances on the surface of the growth images of Paris vietnamensis plants, and generate the analysis data of the removal algorithm of the attached and obstructive substances on the surface of the growth images of Paris vietnamensis plants;

[0068] S4. Based on the Paris polyphylla plant growth image data and the data analyzed by the algorithm for removing obstacles attached to the surface of the Paris polyphylla plant growth image, perform preprocessing for removing obstacles attached to the surface of the plant in the Paris polyphylla plant growth image, and generate preprocessed data of the Paris polyphylla plant growth image;

[0069] S5. Perform identification processing on the pest and disease types of the Paris polyphylla plant by using the Paris polyphylla plant growth image data or the preprocessed data of the Paris polyphylla plant growth image and the pest and disease image data of the Paris polyphylla plant, and generate identification data of the pest and disease types of the Paris polyphylla plant. When there are no pests and diseases, directly execute step S7;

[0070] S6. When there are pests and diseases, perform analysis processing on the pest and disease control methods of the Paris polyphylla plant according to the identification data of the pest and disease types of the Paris polyphylla plant and the pest and disease control method data of the Paris polyphylla plant, and generate analysis data of the pest and disease control methods of the Paris polyphylla plant;

[0071] S7. Construct analysis result data for pest and disease control in Paris polyphylla planting and perform the feedback operation of the analysis result of pest and disease control in Paris polyphylla planting.

[0072] Furthermore, please refer to Figure 1 - Figure 2 , and the operation steps for collecting the Paris polyphylla plant growth image data are as follows:

[0073] S11. Online collect the growth image information of the Paris polyphylla plants in the natural state in the Paris polyphylla planting field through the shooting lens, and generate a set of Paris polyphylla plant growth image data ; where represents the y-th Paris polyphylla plant growth image data collected, represents the maximum value of the number of Paris polyphylla plant growth images.

[0074] Based on the Paris polyphylla plant growth image data and the image data of the obstacles attached to the surface of the Paris polyphylla plant, perform identification processing on the types of obstacles attached to the surface of the plant in the collected Paris polyphylla plant growth image, and generate identification data of the types of obstacles in the Paris polyphylla plant growth image. When there are no obstacles, the operation steps for directly executing step S5 are as follows:

[0075] S21. Establish a set of image data of the obstacles attached to the surface of the Paris polyphylla plant , ; where represents the image data of the obstacles attached to the surface of the Paris polyphylla plant corresponding to the o-th type of obstacle of the Paris polyphylla plant, represents the maximum value of the number of types of obstacles of the Paris polyphylla plant. The types of obstacles of the Paris polyphylla plant include water droplet obstacles, soil particle obstacles, dust obstacles, and withered leaf obstacles; the image data of the obstacles attached to the surface of the Paris polyphylla plant represents the real-scene feature image data of different types of obstacles attached to the surface of the Paris polyphylla plant in the natural state;

[0076] S22. Use the K-D tree nearest neighbor search algorithm to match the Paris polyphylla plant growth image data in the Paris polyphylla plant growth image data set A with the Paris polyphylla plant surface attached obstacle image data in the Paris polyphylla plant surface attached obstacle image data set B, and generate the Paris polyphylla plant growth image obstacle type recognition data according to the image feature matching result; When the image feature matching between the Paris polyphylla plant growth image data in the Paris polyphylla plant growth image data set A and the Paris polyphylla plant surface attached obstacle image data in the Paris polyphylla plant surface attached obstacle image data set B is successful, indicating that the o-th type of Paris polyphylla plant obstacle is attached to the Paris polyphylla plant surface, output the Paris polyphylla plant growth image obstacle type recognition data as there is an obstacle, and output the Paris polyphylla plant obstacle type information; ;

[0077] When and the image feature matching is unsuccessful, indicating that there is no obstacle on the Paris polyphylla plant surface, output the Paris polyphylla plant growth image obstacle type recognition data as there is no obstacle, and directly execute step S5 at this time.

[0078] When there is an obstacle, the operation steps for matching the Paris polyphylla plant growth image obstacle removal algorithm type data according to the Paris polyphylla plant growth image obstacle type recognition data and the Paris polyphylla plant growth image obstacle removal algorithm type data to generate the Paris polyphylla plant growth image obstacle removal algorithm analysis data are as follows: When the Paris polyphylla plant growth image obstacle type recognition data is there is an obstacle, establish a Paris polyphylla plant growth image obstacle removal algorithm type data set

[0079] where

[0080] represents the Paris polyphylla plant growth image obstacle removal algorithm type data corresponding to the o-th type of Paris polyphylla plant obstacle; the Paris polyphylla plant growth image obstacle removal algorithm type data represents the optimal image obstacle removal algorithm type information set for different types of obstacles attached to the Paris polyphylla plant surface; the image obstacle removal algorithm type includes the rasterization-based occlusion culling algorithm, the BSP tree culling algorithm, the PVS culling algorithm, and the ray tracing culling algorithm; When the Paris polyphylla plant growth image obstacle type recognition data is there is an obstacle, establish a Paris polyphylla plant growth image obstacle removal algorithm type data set where

[0081] S32. Use the BERT language model algorithm to match the Paris polyphylla plant growth image obstacle type recognition data with the Paris polyphylla plant growth image obstacle removal algorithm type data in the Paris polyphylla plant growth image obstacle removal algorithm type data set ; Perform keyword matching for the types of obstacles on Paris vietnamensis plants, and search for the recognition data of the types of obstacles on the growth images of Paris vietnamensis plants The corresponding data of the type of obstacle removal algorithm for the growth images of Paris vietnamensis plants , and through data identification, generate the analysis data of the obstacle removal algorithm for the growth images of Paris vietnamensis plants .

[0082] According to the growth image data of Paris vietnamensis plants and the analysis data of the obstacle removal algorithm for the growth images of Paris vietnamensis plants, the operation steps for preprocessing the removal of obstacles attached to the plant surface in the growth images of Paris vietnamensis plants and generating the preprocessing data of the growth images of Paris vietnamensis plants are as follows:

[0083] S41. Adopt the analysis data of the obstacle removal algorithm for the growth images of Paris vietnamensis plants The corresponding image obstacle removal algorithm is used to preprocess the removal of obstacles attached to the plant surface in the growth image data of Paris vietnamensis plants in set A of the growth image data of Paris vietnamensis plants in an orderly manner according to the growth image numbers of Paris vietnamensis plants, and generate a set of preprocessing data of the growth images of Paris vietnamensis plants , where represents the preprocessing data of the y-th growth image of Paris vietnamensis plants

[0084] Through the growth image acquisition unit of Paris vietnamensis plants, the growth image information of Paris vietnamensis plants is accurately and efficiently collected by using a shooting lens, providing reliable data support for the subsequent accurate analysis of the pest and disease information of Paris vietnamensis plants; the obstacle type recognition unit of the growth image of Paris vietnamensis plants, based on the growth image information of Paris vietnamensis plants, combines the intelligent search algorithm and the scientifically stored image information of the obstacles attached to the surface of Paris vietnamensis plants to conduct intelligent analysis of the obstacle types in the growth images of Paris vietnamensis plants, realizing the scientific recognition of the interference characteristics of obstacles in the pest and disease monitoring images of Paris vietnamensis plants and improving the accuracy of pest and disease recognition in Paris vietnamensis plant cultivation; the obstacle removal algorithm matching unit of the growth image of Paris vietnamensis plants, according to the obstacle type recognition information of the growth image of Paris vietnamensis plants, combines the intelligent search algorithm and the preset standard information of the obstacle removal algorithm type of the growth image of Paris vietnamensis plants to conduct precise analysis of the obstacle feature removal algorithm type in the growth image of Paris vietnamensis plants, realizing the scientific matching of the obstacle removal algorithm object based on the interference feature type of the obstacle in the pest and disease monitoring image of Paris vietnamensis plants and improving the quality of the pest and disease monitoring image acquisition of Paris vietnamensis plants; the obstacle preprocessing unit of the growth image of Paris vietnamensis plants independently and efficiently performs the preprocessing of removing the interference characteristics of the obstacles attached to the plant surface in the growth image of Paris vietnamensis plants, realizing the efficient and precise processing of the interference characteristics of the obstacles in the pest and disease monitoring image of Paris vietnamensis plants, improving the authenticity of the pest and disease monitoring image acquisition of Paris vietnamensis plants, and improving the control effect of pests and diseases in Paris vietnamensis plant cultivation

[0085] Further, please refer to Figure 1 - Figure 2, perform pest type identification processing on the Paris vietnamensis plant growth image data or the preprocessed Paris vietnamensis plant growth image data and the Paris vietnamensis plant pest and disease image data to generate Paris vietnamensis plant pest and disease type identification data. When there are no pests and diseases, directly execute the operation steps of step S7 as follows:

[0086] S51. Establish a collection of Paris vietnamensis plant pest and disease image data ;

[0087] where represents the Paris vietnamensis plant pest and disease image data corresponding to the k-th type of Paris vietnamensis plant pest and disease, represents the maximum value of the number of Paris vietnamensis plant pest and disease types. The Paris vietnamensis plant pest and disease types include the pest and disease types of scarab larvae, cutworms, wireworms, leaf miners, damping-off disease, stem rot disease, leaf spot disease, and brown spot disease; the Paris vietnamensis plant pest and disease image data represents the standard image data corresponding to the phenotypic symptoms of different types of Paris vietnamensis plant pests and diseases;

[0088] S52. Use the uniform cost search algorithm to match the Paris vietnamensis plant growth image data in the generated Paris vietnamensis plant growth image data set A or the Paris vietnamensis plant growth image preprocessing data in the Paris vietnamensis plant growth image preprocessing data set with the Paris vietnamensis plant pest and disease image data in the Paris vietnamensis plant pest and disease image data set C for image feature matching, and generate Paris vietnamensis plant pest and disease type identification data based on the image feature matching result ; ;

[0089] When or and are successfully matched in image features, indicating that the Paris vietnamensis plant has the k-th type of Paris vietnamensis plant pest and disease, then output the Paris vietnamensis plant pest and disease type identification data as having pests and diseases, and output the Paris vietnamensis plant pest and disease type information;

[0090] When or and fail to match in image features, indicating that the Paris vietnamensis plant has pests and diseases, then output the Paris vietnamensis plant pest and disease type identification data as having no pests and diseases, and directly execute step S7 at this time.

[0091] 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. The operation steps for generating the Paris polyphylla plant pest and disease control method analysis data are as follows:

[0092] 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;

[0093] 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:

[0094] 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];

[0095] S622, exploration phase, the exploration phase 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 Locate the target and attack. Based on simulating the prevention and control method to identify the movement of the osprey towards the target, update the corresponding prevention and control method to identify the new position of the osprey. The formula for updating the position of the prevention and control method to identify the osprey , where represents the position of the prevention and control method to identify osprey i in the j - dimensional space after update, that is, the position of the prevention and control method to identify osprey i in the polygonatum plant disease and pest control method data set with a space dimension of in the search space; represents the prevention and control method to identify the osprey in the polygonatum plant disease and pest control method data set Search for the polygonatum plant disease and pest control method data that matches the polygonatum plant disease and pest type identification data in the search space at the target position; represents a constant with a value of 1 or 2; if the updated new position is better, replace the initial position before the update of the prevention and control method to identify the osprey according to the exploration - phase position replacement formula. The exploration - phase position replacement formula is , where represents the optimal position of the prevention and control method to identify osprey i in the j - dimensional space in the exploration phase, that is, the position of the prevention and control method to identify osprey i in the polygonatum plant disease and pest control method data set with a space dimension of in the search space that searches for the polygonatum plant disease and pest control method data that most matches the polygonatum plant disease and pest type identification data ; represents the position of the polygonatum plant disease and pest control method data at the position and the fitness value of the polygonatum plant disease and pest control method data at the position with the polygonatum plant disease and pest type identification data ; represents the fitness value of the polygonatum plant disease and pest control method data at the position with the said polygonatum plant disease and pest type identification data ;

[0096] S623. In the exploitation phase, the prevention and control method to identify the osprey hunts and eats the polygonatum plant disease and pest control method data that matches the polygonatum plant disease and pest type identification data in the search space of the polygonatum plant disease and pest control method data set. The exploitation phase of the algorithm for updating the population of the prevention and control method to identify the osprey is modeled based on simulating this natural behavior of the prevention and control method to identify the osprey. Calculate a new random position as suitable for eating the polygonatum plant disease and pest control method data that matches the polygonatum plant disease and pest type identification data target. ​​​Data on the control method for diseases and pests of Paris vietnamensis plants that match The position of the target, calculate new data for identifying the type of diseases and pests of Paris vietnamensis plants suitable for consumption Data on the control method for diseases and pests of Paris vietnamensis plants that match Formula for the position of the target , where Represents the new random position of the osprey i for identifying control methods in the j-dimensional space as the position suitable for edible fish, that is, after the osprey i for identifying control methods is updated, in the space dimension of Set of data on the control method for diseases and pests of Paris vietnamensis plants Search for a new random position in the search space as data for identifying the type of diseases and pests of Paris vietnamensis plants suitable for consumption Data on the control method for diseases and pests of Paris vietnamensis plants that match The position of the target, t represents the current iteration number of the algorithm; if the value of the objective function is improved at this new position, replace the initialization position before the update of the osprey for identifying control methods according to the position replacement formula in the exploitation stage. The position replacement formula in the exploitation stage is , where Represents the optimal position of the osprey i for identifying control methods in the j-dimensional space in the exploitation stage, that is, after the osprey i for identifying control methods is updated, in the space dimension of Set of data on the control method for diseases and pests of Paris vietnamensis plants Optimal position in the search space; Represents Data on the control method for diseases and pests of Paris vietnamensis plants at the position And data for identifying the type of diseases and pests of Paris vietnamensis plants Fitness value;

[0097] S624. When the algorithm meets the maximum number of iterations, output the data on the control method for diseases and pests of Paris vietnamensis plants that most matches the data for identifying the type of diseases and pests of Paris vietnamensis plants Otherwise, continue to execute steps S622 to S623 until the maximum number of iterations is met;

[0098] S625. Construct the data on the control method for diseases and pests of Paris vietnamensis plants output in step S624 Into the analysis data D of the control method for diseases and pests of Paris vietnamensis plants through data identification.

[0099] ​Through the pest and disease type identification unit of Paris vietnamensis plants, the growth image information or preprocessed information of Paris vietnamensis plant growth is combined with the intelligent search algorithm and the pest and disease image information of Paris vietnamensis plants stored based on big data to scientifically and accurately identify the pest and disease types of Paris vietnamensis plants, realizing the intelligent and precise monitoring of pests and diseases in the Paris vietnamensis planting process; the pest and disease control plan analysis unit of Paris vietnamensis plants accurately analyzes the pest and disease control methods of Paris vietnamensis plants according to the pest and disease type identification information of Paris vietnamensis plants combined with the intelligent identification algorithm and the scientifically preset pest and disease control method information of Paris vietnamensis plants, realizing the scientific matching of pest and disease control plans in the Paris vietnamensis planting process, improving the efficiency and reliability of pest and disease control in the Paris vietnamensis planting process, and realizing the intelligent and digital management of Paris vietnamensis planting operations.

[0100] Further, please refer to Figure 1 - Figure 2 , and the operation steps for constructing the pest and disease control analysis result data of Paris vietnamensis planting and performing the feedback operation of the pest and disease control analysis result of Paris vietnamensis planting are as follows:

[0101] S71. Collect and combine the generated data set A of Paris vietnamensis plant growth images or the preprocessed data set of Paris vietnamensis plant growth images, the pest and disease type identification data of Paris vietnamensis plants, and the pest and disease control method analysis data D of Paris vietnamensis plants to construct the pest and disease control analysis result data H of Paris vietnamensis planting, where ;

[0102] S72. Online push the pest and disease control analysis result data H of Paris vietnamensis planting to the pest and disease monitoring end of Paris vietnamensis planting through the Internet of Things communication network and perform the feedback output operation of the pest and disease control analysis result of Paris vietnamensis planting in combination with the display screen.

[0103] Through the construction unit of the pest and disease control analysis result of Paris vietnamensis planting, based on the growth image or preprocessed image of Paris vietnamensis plants, the pest and disease analysis result of Paris vietnamensis plants, and the pest and disease control method analysis result of Paris vietnamensis plants, the pest and disease control analysis result information of Paris vietnamensis planting is efficiently and accurately constructed, realizing the precise and standardized collection of pest and disease control monitoring information in the Paris vietnamensis planting process; the feedback unit of the pest and disease control of Paris vietnamensis plants timely and efficiently pushes the pest and disease control analysis result information of Paris vietnamensis planting to the pest and disease monitoring end of Paris vietnamensis planting through the Internet of Things communication network and performs an intuitive and clear feedback output in cooperation with the display screen, improving the efficiency and quality of pest and disease control monitoring in the Paris vietnamensis planting process.

[0104] Example 2 Please refer to Figure 1 - Figure 2 , a data analysis system for the pest and disease control plan of Paris vietnamensis planting, used to implement a data analysis method for the pest and disease control plan of Paris vietnamensis planting. The system includes a pest and disease image acquisition module for Paris vietnamensis planting, a pest and disease analysis module for Paris vietnamensis planting, and a pest and disease analysis result output module for Paris vietnamensis planting;

[0105] The image acquisition module for pests and diseases in Paris polyphylla Smith cultivation includes an image acquisition unit for the growth of Paris polyphylla Smith plants, an image storage unit for the attached and obstructive substances on the surface of Paris polyphylla Smith plants, an identification unit for the types of obstructive substances in the growth images of Paris polyphylla Smith plants, a storage unit for the algorithms for removing obstructive substances in the growth images of Paris polyphylla Smith plants, a matching unit for the algorithms for removing obstructive substances in the growth images of Paris polyphylla Smith plants, and a preprocessing unit for obstructive substances in the growth images of Paris polyphylla Smith plants;

[0106] The image acquisition unit for the growth of Paris polyphylla Smith plants collects the image data of the growth of Paris polyphylla Smith plants through a shooting lens; the image storage unit for the attached and obstructive substances on the surface of Paris polyphylla Smith plants is used to store the image data of the attached and obstructive substances on the surface of Paris polyphylla Smith plants; the identification unit for the types of obstructive substances in the growth images of Paris polyphylla Smith plants performs identification processing on the types of obstructive substances attached to the surface of the plants in the collected growth images of Paris polyphylla Smith plants based on the growth image data of Paris polyphylla Smith plants and the image data of the attached and obstructive substances on the surface of Paris polyphylla Smith plants, and generates identification data for the types of obstructive substances in the growth images of Paris polyphylla Smith plants; the storage unit for the algorithms for removing obstructive substances in the growth images of Paris polyphylla Smith plants is used to store the data of the types of algorithms for removing obstructive substances in the growth images of Paris polyphylla Smith plants; the matching unit for the algorithms for removing obstructive substances in the growth images of Paris polyphylla Smith plants performs matching processing on the types of algorithms for removing obstructive substances attached to the surface of the plants in the growth images of Paris polyphylla Smith plants according to the identification data for the types of obstructive substances in the growth images of Paris polyphylla Smith plants and the data of the types of algorithms for removing obstructive substances in the growth images of Paris polyphylla Smith plants, and generates analysis data for the algorithms for removing obstructive substances in the growth images of Paris polyphylla Smith plants; the preprocessing unit for obstructive substances in the growth images of Paris polyphylla Smith plants performs preprocessing for removing obstructive substances attached to the surface of the plants in the growth images of Paris polyphylla Smith plants based on the growth image data of Paris polyphylla Smith plants and the analysis data for the algorithms for removing obstructive substances in the growth images of Paris polyphylla Smith plants, and generates preprocessed data for the growth images of Paris polyphylla Smith plants;

[0107] The analysis module for pests and diseases in Paris polyphylla Smith cultivation includes an image storage unit for pests and diseases of Paris polyphylla Smith plants, an identification unit for the types of pests and diseases of Paris polyphylla Smith plants, a storage unit for the control methods of pests and diseases of Paris polyphylla Smith plants, and an analysis unit for the control schemes of pests and diseases of Paris polyphylla Smith plants;

[0108] The image storage unit for pests and diseases of Paris polyphylla Smith plants is used to store the image data of pests and diseases of Paris polyphylla Smith plants; the identification unit for the types of pests and diseases of Paris polyphylla Smith plants performs identification processing on the types of pests and diseases of Paris polyphylla Smith plants by comparing the growth image data of Paris polyphylla Smith plants or the preprocessed data of the growth images of Paris polyphylla Smith plants with the image data of pests and diseases of Paris polyphylla Smith plants, and generates identification data for the types of pests and diseases of Paris polyphylla Smith plants; the storage unit for the control methods of pests and diseases of Paris polyphylla Smith plants is used to store the data of the control methods of pests and diseases of Paris polyphylla Smith plants; the analysis unit for the control schemes of pests and diseases of Paris polyphylla Smith plants performs analysis processing on the control methods of pests and diseases of Paris polyphylla Smith plants according to the identification data for the types of pests and diseases of Paris polyphylla Smith plants and the data of the control methods of pests and diseases of Paris polyphylla Smith plants, and generates analysis data for the control methods of pests and diseases of Paris polyphylla Smith plants;

[0109] The output module for the analysis of diseases and pests in Paris polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz. planting includes the unit for constructing the analysis results of disease and pest control in Paris polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz. planting and the feedback unit for disease and pest control in Paris polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz. planting;

[0110] The unit for constructing the analysis results of disease and pest control in Paris polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz. planting is used to construct the data of the analysis results of disease and pest control in Paris polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz. planting; the feedback unit for disease and pest control in Paris polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz. planting is used to push the analysis results data of disease and pest control in Paris polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz. planting to the monitoring end of diseases and pests in Paris polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz. planting online through the Internet of Things communication network and execute the feedback output operation of the analysis results of disease and pest control in Paris polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz. planting through the display screen.

[0111] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. 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 camera lens, and generating a Paris polyphylla plant growth image data set A = (a1, ..., a y ,…,a ε ), y=1,2,3,…,ε; where a y represents the yth collected growth image data of the Paris polyphylla plant, and ε represents 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, establish the image data set B of the obstructions attached to the surface of the Paris polyphylla plant = (b1,…,b o ,…,b φ ), o=1,2,3,…,φ; where b o 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, and φ represents the maximum number of the Paris polyphylla plant obstruction types; S22, using the KD tree nearest neighbor search algorithm to find the a in A y with the b in the b o Perform image feature matching and generate Paris polyphylla plant growth image occlusion type recognition data A based on the image feature matching results leixing ; when a y With b o If the image feature matching is successful, the Paris polyphylla plant growth image occlusion type identification data A is output. leixing If there is an obstruction, the type of obstruction information of the Paris polyphylla plant is output; when a y With b o If the image features are not matched successfully, the Paris polyphylla plant growth image occlusion type recognition data A is output leixing 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, the A leixing When there are occluders, establish the Paris polyphylla plant growth image occluder removal algorithm type data set B'=(b'1,…,b' o ,…,b' φ ), where b' o 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 leixing and B' in b' o Perform keyword matching of Paris polyphylla plant shielding type and search for the A leixing The corresponding Paris polyphylla plant growth image occlusion removal algorithm type data b' o , and generate the Paris polyphylla plant growth image occlusion removal algorithm analysis data b' through data identification jieguo .

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, using the b' jieguo The corresponding image occlusion removal algorithm is for the a in A y According to the Paris polyphylla plant growth image number, the Paris polyphylla plant growth image is sequentially processed to remove the obstructions attached to the plant surface, and a Paris polyphylla plant growth image preprocessing data set A'=(a'1,...,a' y ,…,a' ε ), where a' y 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, establish Paris polyphylla plant disease and insect pest image data set C = (c1, ..., c k ,…,c η ), k = 1, 2, 3, ..., η; where c k represents the image data of Paris polyphylla plant diseases and insect pests corresponding to the kth Paris polyphylla plant diseases and insect pests type, and η represents the maximum number of Paris polyphylla plant diseases and insect pests types; S52, using a unified cost search algorithm to generate the a in the A y or the a' in the A' y With the C in c k Perform image feature matching and generate Paris polyphylla plant disease and insect pest type identification data C based on the image feature matching results jieguo ; when a y or a' y With c k If the image feature matching is successful, the identification data C of the pest and disease type of the Paris polyphylla plant will be output. jieguo If there are pests and diseases, the pest and disease type information of Paris polyphylla plants will be output; when a y or a' y With c k If the image features are not matched successfully, the identification data C of the pest and disease type of the Paris polyphylla plant is output. jieguo 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 C jieguo When there are pests and diseases, establish a data set of pest and disease control methods for Paris polyphylla plants C' = (c'1, ..., c' k ,…,c' η ), where c' k 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, the Paris polyphylla plant pest type identification data C jieguo The Paris polyphylla plant pest control method data set C' k 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 phase, the exploration phase of updating the control method identification osprey population is based on the simulation of the natural behavior of the control method identification osprey. The control method identification osprey randomly detects the same plant disease and insect pest type identification data C in the search space of the Paris polyphylla plant disease and insect pest control method data set C'. jieguo Matched data c' of pest control methods for Paris polyphylla plants k 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 hunts and eats the Paris polyphylla plant pest and disease type identification data set C' in the search space of the Paris polyphylla plant pest and disease prevention method data set C'. jieguo Matched data c' of pest control methods for Paris polyphylla plants k 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 C jieguo Matched data c' of pest control methods for Paris polyphylla plants k The target location is calculated, and the new suitable edible and pest identification data C of the Paris polyphylla plant is calculated. jieguo Matched data c' of pest control methods for Paris polyphylla plants k 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 C of the pest and disease type of the Paris polyphylla plant jieguo The most matching data of pest control methods for Paris polyphylla plants c' k , otherwise continue to execute steps S622 to S623 until the maximum number of iterations is met; S625, the Paris polyphylla plant pest control method data c' output in step S624 k 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 A', the C jieguo , 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 includes 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; 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.

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