Monitoring, prevention and control method and system based on pitaya pest occurrence rule

Abstract

The invention discloses a monitoring, prevention and control method and system based on a pitaya pest occurrence rule, and the method comprises the steps: retrieving and obtaining the historical pest occurrence data of pitaya, carrying out the data preprocessing, and dividing and generating a pest occurrence data subset with a pest category label; analyzing occurrence cause features corresponding to the pest data samples and a causal relationship between the features in the pest occurrence data subset, and constructing a pest attribution path; the similarity between the multi-source monitoring data in the monitoring area and the pest attribution path is obtained, and pitaya pest early warning of the monitoring area is generated through the similarity; and extracting a pest category corresponding to pitaya pest early warning and a predicted hazard condition, and analyzing to obtain a prevention and control method combination. The invention provides an accurate monitoring and early warning model for the pitaya pests, and formulates a scientific prevention and control scheme in a targeted manner according to the predicted hazard condition, thereby improving the prevention and control effect of the pitaya pests and effectively improving the quality of the pitaya.

Description

Technical Field

[0001] The present invention relates to the technical field of pest control, and more specifically, to a monitoring and control method and system based on the occurrence law of dragon fruit pests. Background Art

[0002] Dragon fruit is rich in nutrients and has high nutritional value. There are many challenges in the cultivation process of dragon fruit, especially since it needs to grow in high temperature and high humidity areas. It is very susceptible to diseases and insect pests, which cause a variety of diseases and insect pests. These diseases and insect pests seriously affect the growth of dragon fruit plants, fruit quality and economic benefits. Harmful organisms include pathogens and pests that cause diseases and insect pests, as well as mollusks such as snails and slugs. In order to reduce the impact of diseases and insect pests on the quality and yield of dragon fruit, corresponding measures must be taken to prevent and control diseases and insect pests. Although a variety of prevention and control measures have been used to prevent and control dragon fruit diseases and insect pests, there are still certain limitations.

[0003] At present, agricultural production is still in the stage of using a large number of fertilizers and pesticides, mainly relying on traditional chemical control methods to control crop diseases and insect pests. The abuse and misuse of pesticides have brought about increasingly serious problems such as reduced biodiversity, increased pest resistance, pesticide residues and environmental pollution. In addition, the traditional prediction of dragon fruit pests is mainly based on the experience of growers, who judge the possibility and type of pests and diseases, and then formulate corresponding prevention and control measures. This method of relying on experience judgment is highly subjective, has a low accuracy rate, and is prone to waste and loss of human and material resources. Therefore, how to accurately predict the occurrence trend of dragon fruit pests and formulate personalized prevention and control plans to improve the efficiency of pest control is an urgent problem to be solved. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a monitoring and prevention method and system based on the occurrence laws of pitaya pests, with the aim of analyzing the occurrence laws of the main diseases and pests of pitaya pests, accurately preventing and controlling pitaya pests, and providing a reference for pitaya pest prevention and control.

[0005] The first aspect of the present invention provides a monitoring and control method based on the occurrence law of dragon fruit pests, comprising:

[0006] Retrieving and obtaining historical pest occurrence data of pitaya for data preprocessing, dividing the historical pest occurrence data according to pest categories, and generating a subset of pest occurrence data with pest category labels;

[0007] Extracting features from the pest occurrence data subset, analyzing occurrence causal features corresponding to the pest data sample and the causal relationship between the features, and constructing a pest attribution path based on the occurrence causal features and the causal relationship;

[0008] Acquire multi-source monitoring data in the monitoring area, construct a multi-source monitoring data sequence according to the multi-source monitoring data, calculate the similarity between the multi-source monitoring data sequence and the pest attribution path, and generate a pitaya pest warning in the monitoring area according to the similarity;

[0009] Extract the pest categories and predicted damage conditions corresponding to the dragon fruit pest warning, and obtain the combination of prevention and control methods based on the pest categories and predicted damage conditions.

[0010] In this solution, the historical pest occurrence data is divided according to pest categories to generate a subset of pest occurrence data with pest category labels, specifically:

[0011] Acquire historical pest occurrence data after data cleaning and normalization, obtain pest category keywords in the historical pest occurrence data by keyword extraction, and calculate semantic similarity between keywords of different pest categories;

[0012] The extracted pest category keywords are simplified by using the semantic similarity, and pest category keywords with the same semantics are merged to generate pest category labels after redundancy removal;

[0013] Clustering is performed using the pest category labels to obtain pest occurrence data subsets under each pest category label, and the number of pest data samples contained in different pest occurrence data subsets is counted to screen pest occurrence data subsets whose number of pest data samples does not meet a preset number standard;

[0014] Upsampling is performed on the pest occurrence data subset obtained through screening, and data balancing is performed. After the sample quantity reaches the standard, the pest occurrence data subset with pest category labels is output.

[0015] In this solution, feature extraction is performed in the pest occurrence data subset, and the occurrence causal features corresponding to the pest data samples are analyzed, specifically:

[0016] Acquire pest occurrence related data in each pest occurrence data subset to generate pest data samples, read the data variation range according to the pest data samples, preliminarily select the influencing factors of different dragon fruit pests according to the data variation range, and perform variance filtering on the preliminarily selected influencing factors to generate an influencing factor set;

[0017] Extracting features from the influencing factor set by a genetic algorithm, initializing the influencing factor set to generate an initial population, encoding the initial population, training the pest prediction model by a cross-validation method, and calculating the average accuracy as the fitness value of the individual;

[0018] Based on the fitness value, a preset number of individuals are selected from the population according to an elite selection method, and differential calculation is introduced to generate the direction of crossover and mutation, the population is updated through mutation and differential operations, and a subset of influencing factors is output when a preset minimum fitness threshold is reached;

[0019] Retraining the pest prediction model based on the influencing factor subset, using the SVM training model to obtain the mean square error of each influencing factor as an influencing factor score, further screening the influencing factors according to the influencing factor score, and obtaining a new influencing factor subset;

[0020] The pest prediction model is trained again using the new subset of influencing factors until the model obtains the optimal value, and then the final subset of influencing factors is output to obtain the occurrence causal characteristics corresponding to the corresponding pest category label.

[0021] In this scheme, the causal relationship between the occurrence causal characteristics is obtained, specifically:

[0022] Obtain the occurrence causal characteristics corresponding to different pest categories, initially define the information flow path between the occurrence causal characteristics according to the correlation between the occurrence causal characteristics, and calculate the transfer entropy between each occurrence causal characteristic node in different information flow paths;

[0023] The sum of the transfer entropies between the causal feature nodes in the information flow path is obtained, and the information flow path with the largest sum of transfer entropies is selected. The causal relationship between the causal features in the process of pest occurrence is characterized according to the selected information flow path.

[0024] In this scheme, the pest attribution path is constructed based on the occurrence causal characteristics and the causal relationship, specifically:

[0025] Obtain the critical value interval of the corresponding causal characteristics when pitaya pests occur according to the historical pest occurrence data, and determine the probability of pest occurrence when the corresponding parameters of the causal characteristics are within the critical value interval based on the historical pitaya planting data;

[0026] Generate weight information of each occurrence causal feature according to the probability of occurrence of the pest, obtain the information flow path corresponding to the causal relationship, match the weight information with the occurrence causal feature node, and mark the critical value interval of the causal relationship to characterize the attribute information of the causal relationship;

[0027] The pest attribution path is generated using the information flow path after tag matching.

[0028] In this scheme, the similarity between the multi-source monitoring data sequence and the pest attribution path is calculated, and the pitaya pest warning in the monitoring area is generated by the similarity, specifically:

[0029] Obtain the occurrence causal characteristics corresponding to different pest categories, aggregate and remove redundancy of all occurrence causal characteristics, generate monitoring data categories of pitaya planting areas, obtain multi-source monitoring data based on the monitoring data categories, and construct a multi-source monitoring data sequence after pre-processing and combining with time series;

[0030] Based on the pest attribution path, the occurrence causative feature nodes falling into the critical value interval are obtained according to the multi-source monitoring data sequence, and the highly correlated occurrence causative feature nodes corresponding to the occurrence causative feature nodes falling into the critical value interval are obtained according to the connection relationship of the pest attribution path;

[0031] Path fitting is performed based on the occurrence causal characteristic nodes and the highly correlated occurrence causal characteristic nodes that fall into the critical value interval, and the fitted path is segmented. In each segment, the weighted Fréchet distance of the node is used as the basis for similarity quantification, and the average similarity between the fitted path and the pest attribution path is calculated;

[0032] The pest category corresponding to the pest attribution path with an average similarity greater than a preset similarity threshold is obtained, and the damage status is obtained according to the symptoms of the pests in the pitaya plant, and a pitaya pest warning is generated according to the pest category and the damage status.

[0033] In this plan, a combination of control methods is obtained based on the pest category and predicted damage status, specifically:

[0034] The corresponding pest category and hazard status are read through the dragon fruit pest early warning, the monitoring data change characteristics are extracted according to the multi-source monitoring data sequence, the mapping relationship between the multi-source monitoring data and the pest hazard status is trained using the historical pest occurrence data, and the predicted hazard status after a preset time is obtained according to the mapping relationship;

[0035] According to the predicted hazard status, it is judged whether it exceeds the preset hazard status range. If it does not exceed, the green control measures of the pest category are selected through the relevant knowledge graph, and the prevention and control examples of each green control measure are retrieved and obtained. Based on the prevention and control examples, the average prevention and control time is obtained, and the ranking of green control measures is generated;

[0036] By predicting the hazard status and evaluating the prevention and control time as the prevention and control time standard, a preset number of green prevention and control measures that meet the prevention and control time standard are selected according to the green prevention and control measures ranking to combine and generate a prevention and control method combination;

[0037] When it exceeds the limit, the recommended pesticides for the pest category are selected through the relevant knowledge graph, and the corresponding relationship between different pesticide concentrations and average control time is obtained based on the predicted hazard status;

[0038] The longest control time is obtained according to the control time standard, and the drug concentration for the longest control time is obtained through the corresponding relationship. The recommended agents and drug concentrations are used to generate chemical control measures, which are combined with green control measures with the shortest average control time to generate a control method combination.

[0039] The second aspect of the present invention provides a monitoring and prevention system based on the occurrence law of dragon fruit pests, including a pest occurrence law analysis module, a multi-source monitoring data acquisition module, a dragon fruit pest early warning module, a prevention and control decision module and a data visualization module;

[0040] The pest occurrence law analysis module is responsible for analyzing the occurrence causal characteristics corresponding to different pest categories and the causal relationship between the characteristics according to the historical pest occurrence data, constructing the corresponding pest attribution path, and characterizing the occurrence law of pitaya pests;

[0041] The multi-source monitoring data acquisition module is responsible for collecting multi-source monitoring data of the monitoring area, and performing serialization processing to construct a multi-source monitoring data sequence with the same structure as the pest attribution path;

[0042] The pitaya pest early warning module is responsible for calculating the similarity between the multi-source monitoring data sequence and the attribution paths of different pests, and generating a pitaya pest early warning based on the similarity judgment;

[0043] The control decision module is responsible for obtaining a combination of control methods dominated by green control measures based on the early warning pest categories and predicted hazard conditions;

[0044] The data visualization module is responsible for displaying the combination of prevention and control methods in the monitored area, and for real-time visualization of multi-source monitoring data and dragon fruit pest warning.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention provides an accurate monitoring and early warning model for pitaya pest damage, scientifically guides pest control, and formulates a scientific control plan in a targeted manner according to the predicted damage conditions, thereby improving the pitaya pest control effect.

[0047] By formulating scientific prevention and control plans based on green control technology, the ability of pitaya growers to emergency control pests can be enhanced, and the pollution caused to orchards by pesticides and their waste can be reduced. By increasing cultivation management improvements, the growers' extensive planting methods can be gradually changed, effectively improving the quality of pitaya. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments or exemplary embodiments of the present invention, the drawings required for use in the embodiments or exemplary descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained according to the drawings without paying creative work.

[0049] Figure 1 The flowchart of the monitoring and control method of the present invention based on the occurrence law of dragon fruit harmful organisms is shown;

[0050] Figure 2 A flow chart of obtaining occurrence cause characteristics according to an embodiment of the present invention is shown;

[0051] Figure 3 A flow chart of generating a warning of pitaya pests in a monitoring area according to an embodiment of the present invention is shown;

[0052] Figure 4 The block diagram of the monitoring and prevention system of the present invention based on the occurrence law of dragon fruit pests is shown. DETAILED DESCRIPTION

[0053] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0055] Figure 1 The flowchart of the monitoring and control method of the present invention based on the occurrence law of dragon fruit pests is shown.

[0056] like Figure 1 As shown, a first embodiment of the present invention provides a monitoring and control method based on the occurrence law of dragon fruit pests, including:

[0057] S102, retrieving and obtaining historical pest occurrence data of pitaya for data preprocessing, dividing the historical pest occurrence data according to pest categories, and generating a subset of pest occurrence data with pest category labels;

[0058] S104, extracting features from the pest occurrence data subset, analyzing occurrence causal features corresponding to the pest data sample and the causal relationship between the features, and constructing a pest attribution path based on the occurrence causal features and the causal relationship;

[0059] S106, acquiring multi-source monitoring data in the monitoring area, constructing a multi-source monitoring data sequence according to the multi-source monitoring data, calculating the similarity between the multi-source monitoring data sequence and the pest attribution path, and generating a pitaya pest warning in the monitoring area according to the similarity;

[0060] S108, extracting the pest category and predicted damage status corresponding to the dragon fruit pest warning, and obtaining a combination of prevention and control methods according to the pest category and predicted damage status.

[0061] It should be noted that the main pests of pitaya are anthracnose, canker, stem rot, black spot, aphids, thrips, cotton bollworm, Spodoptera litura, beet armyworm, fruit fly, scale insects, ants, snails, slugs, etc. The historical pest occurrence data of pitaya are obtained and pre-processed by data cleaning, data dimension reduction, normalization, etc. The pest category keywords in the historical pest occurrence data are obtained by keyword extraction, and the semantic similarity between different pest category keywords is calculated to reduce the category label redundancy caused by differences in pest names. The extracted pest category keywords are streamlined by using the semantic similarity, and the pest category keywords with the same semantics are merged to generate pest category labels after redundancy processing; the pest category labels are used for clustering to obtain pest occurrence data subsets under each pest category label, and the number of pest data samples contained in different pest occurrence data subsets is counted, and pest occurrence data subsets whose number of pest data samples does not meet the preset quantity standard are screened; the SMOTE method is used to upsample the screened pest occurrence data subsets, and data balancing is performed. After the sample number meets the standard, the pest occurrence data subset with the pest category label is output.

[0062] Figure 2 A flow chart of obtaining occurrence cause characteristics according to an embodiment of the present invention is shown.

[0063] According to an embodiment of the present invention, feature extraction is performed in the pest occurrence data subset, and occurrence cause features corresponding to the pest data samples are analyzed, specifically:

[0064] S202, obtaining pest occurrence related data in each pest occurrence data subset to generate a pest data sample, reading the data variation range according to the pest data sample, preliminarily selecting influencing factors of different dragon fruit pests according to the data variation range, and performing variance filtering on the preliminarily selected influencing factors to generate an influencing factor set;

[0065] S204, extracting features from the influencing factor set by a genetic algorithm, initializing the influencing factor set to generate an initial population, encoding the initial population, training the pest prediction model by a cross-validation method, and calculating the average accuracy as the fitness value of the individual;

[0066] S206, selecting a preset number of individuals from the population according to the elite selection method based on the fitness value, and introducing differential calculation to generate crossover and mutation directions, updating the population through mutation and differential operations, and outputting a subset of influencing factors when a preset minimum fitness threshold is reached;

[0067] S208, retraining the pest prediction model based on the influencing factor subset, using the SVM training model to obtain the mean square error of each influencing factor as an influencing factor score, further screening the influencing factors according to the influencing factor score, and obtaining a new influencing factor subset;

[0068] S210, use the new subset of influencing factors to train the pest prediction model again until the model obtains the optimal value, and then output the final subset of influencing factors to obtain the occurrence causal characteristics corresponding to the corresponding pest category label.

[0069] It should be noted that the feature selection method combining variance filtering and improved cross-validation recursive feature elimination based on genetic algorithm is used to search for a subset of factors affecting different pests of pitaya. In the genetic algorithm, the fitness value of each individual is calculated by cross-validation, and the influencing factor set is divided into n folds. The pest prediction model is used on the n-1 folds and tested on the remaining folds. The average accuracy is calculated after repeated iterations. According to the elite selection method, a preset number of elite individuals with the highest fitness values ​​are selected from the population. The idea of ​​inter-individual differences is used to increase information exchange between elite individuals, and the direction of crossover and mutation is clarified. For example, the difference between the suboptimal solution and the optimal solution and the difference between the third optimal solution and the optimal solution are used to obtain the mutated individuals. According to the direction of crossover and mutation in the population, the optimal solution in the selected feature number is approached. After reaching the minimum fitness threshold, the iteration is stopped. In each generation of genetic operations, the individual with the highest fitness is selected to generate a subset of influencing factors.

[0070] It should be noted that the occurrence causal characteristics corresponding to different pest categories are obtained, and the information flow path between the occurrence causal characteristics is initially defined according to the correlation between the occurrence causal characteristics, and two occurrence causal characteristics with satisfactory correlation are connected. According to the connection relationship between different characteristics, all possible information transmission paths and transmission directions are obtained, and the transfer entropy between each occurrence causal characteristic node in different information flow paths is calculated in all information flow paths; the transfer entropy characterizes the information transfer between two system processes through information entropy. The sum of the transfer entropies between each occurrence causal characteristic node in the information flow path is obtained, and the information flow path with the largest sum of transfer entropies is selected. The causal relationship between the occurrence causal characteristics in the pest occurrence process is characterized according to the selected information flow path.

[0071] According to the historical pest occurrence data, the critical value interval of the corresponding occurrence causal characteristics when the pitaya pest occurs is obtained, and the probability of pest occurrence when the corresponding parameters of the occurrence causal characteristics are in the critical value interval is judged based on the historical pitaya planting data; the weight information of each occurrence causal characteristic is generated according to the pest occurrence probability, the information flow path corresponding to the causal relationship is obtained, the weight information is matched with the occurrence causal characteristic node, and the critical value interval is marked for the causal relationship, the attribute information of the causal relationship is characterized, and the pest attribution path is generated using the information flow path after the mark matching; for example, if the overwintering pests in the tree are not cleared in time, when the environmental temperature and humidity rise to the critical value interval, the pests hatch and cause losses to the pitaya. In addition, the pathogens overwinter in the diseased part or diseased residue as mycelium or conidia, and the conidia of the pathogens are spread by wind, rain or insects. When the suitable temperature for the growth of the pathogens reaches the critical value interval (20-30°C), it is easy to cause pitaya anthracnose.

[0072] Figure 3 A flow chart of generating a warning of dragon fruit pests in a monitoring area according to an embodiment of the present invention is shown.

[0073] According to an embodiment of the present invention, the similarity between the multi-source monitoring data sequence and the pest attribution path is calculated, and the pitaya pest warning in the monitoring area is generated by the similarity, specifically:

[0074] S302, obtaining occurrence causal features corresponding to different pest categories, aggregating and de-redundanting all occurrence causal features, generating monitoring data categories of pitaya planting areas, obtaining multi-source monitoring data based on the monitoring data categories, and constructing a multi-source monitoring data sequence after pre-processing and combining with time series;

[0075] S304, based on the pest attribution path, obtaining occurrence causative feature nodes falling within the critical value interval according to the multi-source monitoring data sequence, and obtaining highly correlated occurrence causative feature nodes corresponding to the occurrence causative feature nodes falling within the critical value interval according to the connection relationship of the pest attribution path;

[0076] S306, performing path fitting according to occurrence causal characteristic nodes and highly correlated occurrence causal characteristic nodes falling within the critical value interval, segmenting the fitted path, and within each segment, using the node weighted Fréchet distance as a similarity quantification basis, calculating the average similarity between the fitted path and the pest attribution path;

[0077] S308, obtaining the pest category corresponding to the pest attribution path with an average similarity greater than a preset similarity threshold, and obtaining the damage status according to the symptoms of the pests in the pitaya plant, and generating a pitaya pest warning according to the pest category and the damage status.

[0078] It should be noted that the fitted path is segmented, and in each segment, the corresponding occurrence-causing feature node pairs (p a1 ,q b1 ),(p a2 ,q b2 ),…,(p at ,q bt ), a1=1, b1=1, at=m, bt=n, p at Indicates the occurrence-causing characteristic nodes or highly correlated occurrence-causing characteristic nodes that fall into the critical value interval in the fitting path, q bt represents the occurrence of causal characteristic nodes in the pest attribution path, m and n represent the path lengths of the fitting path and the pest attribution path respectively; the length of the occurrence causal characteristic node pair is: d represents the Euclidean distance, and the Fleche distance F in each segment is expressed as F = min||D||. The Fleche distance of the node pairs is weighted using the weight information of the occurrence causal characteristics in the pest attribution path, which is used as the basis for quantifying the similarity, and the average similarity of each node pair is obtained to represent the similarity between the paths.

[0079] It should be noted that the corresponding pest category and hazard status are read through the pitaya pest early warning. The monitoring data categories include temperature, rainfall, sunshine, air and soil moisture, acidity and alkalinity, plant images and production archives, etc. The monitoring data change characteristics are extracted according to the multi-source monitoring data sequence, and the mapping relationship between multi-source monitoring data and pest hazard status is trained based on deep learning methods such as neural networks using historical pest occurrence data. The predicted hazard status after a preset time is obtained according to the mapping relationship; according to the predicted hazard status, it is judged whether it exceeds the preset hazard status range. When it does not exceed the range, the green prevention and control measures of the pest category are selected through the relevant knowledge graph. The relevant knowledge graph includes green prevention and control measures such as light trapping, color plate trapping, food trapping, biological control and water and fertilizer improvement. Obtain prevention and control examples for each green prevention and control measure, obtain the average prevention and control time based on the prevention and control examples, and generate a ranking of green prevention and control measures; evaluate the prevention and control time by predicting the hazard status as the prevention and control time standard, select a preset number of green prevention and control measures that meet the prevention and control time standard according to the ranking of green prevention and control measures, and combine them to generate a combination of prevention and control methods; when it exceeds, select the recommended pesticides of the pest category through the relevant knowledge graph, and obtain the corresponding relationship between different drug concentrations and the average prevention and control time according to the predicted hazard status; obtain the longest prevention and control time according to the prevention and control time standard, obtain the drug concentration for the longest prevention and control time through the corresponding relationship, generate chemical prevention and control measures with the recommended drugs and drug concentrations, and combine them with the green prevention and control measures with the shortest average prevention and control time to generate a combination of prevention and control methods.

[0080] The production archives of the pitaya planting process are obtained through multi-source monitoring data in the monitoring area, and the generated archives include water and fertilizer management, pest control, pruning and harvesting measures. The impact of the current planting method on pitaya pests is evaluated according to the production archives, and the probability of occurrence of pitaya pests is predicted according to the production archives. The probability of occurrence of pests is sent to the grower terminal in a preset manner, and the cultivation management measures currently required in the monitoring area are obtained according to the multi-source monitoring data, and the growers are urged to implement the cultivation management measures through data monitoring. If the growers fail to implement the cultivation management measures within the preset time period, the monitoring frequency is increased, and the probability of occurrence of pitaya pests is updated in time for push, and the extensive management planting method of the growers is gradually changed by increasing the means of cultivation management improvement.

[0081] Figure 4 The block diagram of the monitoring and prevention system of the present invention based on the occurrence law of dragon fruit pests is shown.

[0082] The second aspect of the present invention provides a monitoring and prevention system 4 based on the occurrence law of dragon fruit pests, including a pest occurrence law analysis module 41, a multi-source monitoring data acquisition module 42, a dragon fruit pest early warning module 43, a prevention and control decision module 44 and a data visualization module 45;

[0083] The pest occurrence law analysis module is responsible for analyzing the occurrence causal characteristics corresponding to different pest categories and the causal relationship between the characteristics according to the historical pest occurrence data, constructing the corresponding pest attribution path, and characterizing the occurrence law of pitaya pests;

[0084] The multi-source monitoring data acquisition module is responsible for collecting multi-source monitoring data of the monitoring area, and performing serialization processing to construct a multi-source monitoring data sequence with the same structure as the pest attribution path;

[0085] The pitaya pest early warning module is responsible for calculating the similarity between the multi-source monitoring data sequence and the attribution paths of different pests, and generating a pitaya pest early warning based on the similarity judgment;

[0086] The control decision module is responsible for obtaining a combination of control methods dominated by green control measures based on the early warning pest categories and predicted hazard conditions;

[0087] The data visualization module is responsible for displaying the combination of prevention and control methods in the monitored area, and for real-time visualization of multi-source monitoring data and dragon fruit pest warning.

[0088] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0089] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0090] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0091] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0092] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0093] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A monitoring and prevention method based on the occurrence law of dragon fruit pests, It is characterized in that The following steps are involved: Retrieving and obtaining historical pest occurrence data of pitaya for data preprocessing, dividing the historical pest occurrence data according to pest categories, and generating a subset of pest occurrence data with pest category labels; Extracting features from the pest occurrence data subset, analyzing occurrence causal features corresponding to the pest data sample and the causal relationship between the features, and constructing a pest attribution path based on the occurrence causal features and the causal relationship; Acquire multi-source monitoring data in the monitoring area, construct a multi-source monitoring data sequence according to the multi-source monitoring data, calculate the similarity between the multi-source monitoring data sequence and the pest attribution path, and generate a pitaya pest warning in the monitoring area according to the similarity; Extract the pest categories and predicted damage conditions corresponding to the dragon fruit pest warning, and obtain the combination of prevention and control methods based on the pest categories and predicted damage conditions.

2. A monitoring and control method based on the occurrence law of dragon fruit harmful organisms according to claim 1, It is characterized in that The historical pest occurrence data is divided according to pest categories to generate a subset of pest occurrence data with pest category labels, specifically: Acquire historical pest occurrence data after data cleaning and normalization, obtain pest category keywords in the historical pest occurrence data by keyword extraction, and calculate semantic similarity between keywords of different pest categories; The extracted pest category keywords are simplified by using the semantic similarity, and pest category keywords with the same semantics are merged to generate pest category labels after redundancy removal; Clustering is performed using the pest category labels to obtain pest occurrence data subsets under each pest category label, and the number of pest data samples contained in different pest occurrence data subsets is counted to screen pest occurrence data subsets whose number of pest data samples does not meet a preset number standard; Upsampling is performed on the pest occurrence data subset obtained through screening, and data balancing is performed. After the sample quantity reaches the standard, the pest occurrence data subset with pest category labels is output.

3. A monitoring and control method based on the occurrence law of dragon fruit harmful organisms according to claim 1, It is characterized in that Feature extraction is performed in the pest occurrence data subset, and the occurrence cause characteristics corresponding to the pest data samples are analyzed, specifically: Acquire pest occurrence related data in each pest occurrence data subset to generate pest data samples, read the data variation range according to the pest data samples, preliminarily select the influencing factors of different dragon fruit pests according to the data variation range, and perform variance filtering on the preliminarily selected influencing factors to generate an influencing factor set; Extracting features from the influencing factor set by a genetic algorithm, initializing the influencing factor set to generate an initial population, encoding the initial population, training the pest prediction model by a cross-validation method, and calculating the average accuracy as the fitness value of the individual; Based on the fitness value, a preset number of individuals are selected from the population according to an elite selection method, and differential calculation is introduced to generate the direction of crossover and mutation, the population is updated through mutation and differential operations, and a subset of influencing factors is output when a preset minimum fitness threshold is reached; Retraining the pest prediction model based on the influencing factor subset, using the SVM training model to obtain the mean square error of each influencing factor as an influencing factor score, further screening the influencing factors according to the influencing factor score, and obtaining a new influencing factor subset; The pest prediction model is trained again using the new subset of influencing factors until the model obtains the optimal value, and then the final subset of influencing factors is output to obtain the occurrence causal characteristics corresponding to the corresponding pest category label.

4. A monitoring and control method based on the occurrence law of dragon fruit harmful organisms according to claim 3, It is characterized in that Obtain the causal relationship between the occurrence causal characteristics, specifically: Obtain the occurrence causal characteristics corresponding to different pest categories, initially define the information flow path between the occurrence causal characteristics according to the correlation between the occurrence causal characteristics, and calculate the transfer entropy between each occurrence causal characteristic node in different information flow paths; The sum of the transfer entropies between the causal feature nodes in the information flow path is obtained, and the information flow path with the largest sum of transfer entropies is selected. The causal relationship between the causal features in the process of pest occurrence is characterized according to the selected information flow path.

5. A monitoring and control method based on the occurrence law of dragon fruit harmful organisms according to claim 1, It is characterized in that The pest attribution path is constructed based on the occurrence causal characteristics and the causal relationship, specifically: Obtain the critical value interval of the corresponding causal characteristics when pitaya pests occur according to the historical pest occurrence data, and determine the probability of pest occurrence when the corresponding parameters of the causal characteristics are within the critical value interval based on the historical pitaya planting data; Generate weight information of each occurrence causal feature according to the probability of occurrence of the pest, obtain the information flow path corresponding to the causal relationship, match the weight information with the occurrence causal feature node, and mark the critical value interval of the causal relationship to characterize the attribute information of the causal relationship; The pest attribution path is generated using the information flow path after tag matching.

6. A monitoring and control method based on the occurrence law of dragon fruit harmful organisms according to claim 1, It is characterized in that The similarity between the multi-source monitoring data sequence and the pest attribution path is calculated, and the pitaya pest warning in the monitoring area is generated through the similarity, specifically: Obtain the occurrence causal characteristics corresponding to different pest categories, aggregate and remove redundancy of all occurrence causal characteristics, generate monitoring data categories of pitaya planting areas, obtain multi-source monitoring data based on the monitoring data categories, and construct a multi-source monitoring data sequence after pre-processing and combining with time series; Based on the pest attribution path, the occurrence causative feature nodes falling into the critical value interval are obtained according to the multi-source monitoring data sequence, and the highly correlated occurrence causative feature nodes corresponding to the occurrence causative feature nodes falling into the critical value interval are obtained according to the connection relationship of the pest attribution path; Path fitting is performed based on the occurrence causal characteristic nodes and the highly correlated occurrence causal characteristic nodes that fall into the critical value interval, and the fitted path is segmented. In each segment, the weighted Fréchet distance of the node is used as the basis for similarity quantification, and the average similarity between the fitted path and the pest attribution path is calculated; The pest category corresponding to the pest attribution path with an average similarity greater than a preset similarity threshold is obtained, and the damage status is obtained according to the symptoms of the pests in the pitaya plant, and a pitaya pest warning is generated according to the pest category and the damage status.

7. A monitoring and control method based on the occurrence law of dragon fruit harmful organisms according to claim 1, It is characterized in that Obtain a combination of control methods based on pest categories and predicted damage conditions, specifically: The corresponding pest category and hazard status are read through the dragon fruit pest early warning, the monitoring data change characteristics are extracted according to the multi-source monitoring data sequence, the mapping relationship between the multi-source monitoring data and the pest hazard status is trained using the historical pest occurrence data, and the predicted hazard status after a preset time is obtained according to the mapping relationship; According to the predicted hazard status, it is judged whether it exceeds the preset hazard status range. If it does not exceed, the green control measures of the pest category are selected through the relevant knowledge graph, and the prevention and control examples of each green control measure are retrieved and obtained. Based on the prevention and control examples, the average prevention and control time is obtained, and the ranking of green control measures is generated; By predicting the hazard status and evaluating the prevention and control time as the prevention and control time standard, a preset number of green prevention and control measures that meet the prevention and control time standard are selected according to the green prevention and control measures ranking to combine and generate a prevention and control method combination; When it exceeds the limit, the recommended pesticides for the pest category are selected through the relevant knowledge graph, and the corresponding relationship between different pesticide concentrations and average control time is obtained based on the predicted hazard status; The longest control time is obtained according to the control time standard, and the drug concentration for the longest control time is obtained through the corresponding relationship. The recommended agents and drug concentrations are used to generate chemical control measures, which are combined with green control measures with the shortest average control time to generate a control method combination.

8. A monitoring and prevention system based on the occurrence law of dragon fruit pests, It is characterized in that It includes the pest occurrence law analysis module, multi-source monitoring data collection module, dragon fruit pest early warning module, prevention and control decision module and data visualization module; The pest occurrence law analysis module is responsible for analyzing the occurrence causal characteristics corresponding to different pest categories and the causal relationship between the characteristics according to the historical pest occurrence data, constructing the corresponding pest attribution path, and characterizing the occurrence law of pitaya pests; The multi-source monitoring data acquisition module is responsible for collecting multi-source monitoring data of the monitoring area, and performing serialization processing to construct a multi-source monitoring data sequence with the same structure as the pest attribution path; The pitaya pest early warning module is responsible for calculating the similarity between the multi-source monitoring data sequence and the attribution paths of different pests, and generating a pitaya pest early warning based on the similarity judgment; The control decision module is responsible for obtaining a combination of control methods dominated by green control measures based on the early warning pest categories and predicted hazard conditions; The data visualization module is responsible for displaying the combination of prevention and control methods in the monitored area, and for real-time visualization of multi-source monitoring data and dragon fruit pest warning.

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