Comprehensive prevention and control method for combination of efficient insect-resistant endophytes and functional plants

Abstract

The invention relates to the technical field of pest prevention and control, in particular to a comprehensive prevention and control method for efficient insect-resistant endophyte and functional plant combination, which comprises the following steps of: acquiring multi-source data in real time through a sensor network, and performing data cleaning, standardization and feature extraction on the multi-source data; the synergistic effect of endophytes and functional plants is analyzed by using multiple nonlinear regression and a support vector machine algorithm, and the influence of each factor on the pest prevention and control effect is quantified. Based on a synergistic effect analysis result, an agricultural production cycle, crop types, climate changes and other factors, a personalized prevention and control strategy is dynamically generated, meanwhile, the analysis result and the prevention and control strategy are displayed through a visual interface, real-time decision support is provided for farm management personnel, and a scheme is dynamically adjusted according to environmental changes and prevention and control effects. The precision and efficiency of pest control can be effectively improved, the use of chemical pesticides is reduced, and green agricultural production is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of insect pest control, and in particular to a comprehensive control method combining highly effective insect-resistant endophytes with functional plants. Background Art

[0002] With the continuous expansion of agricultural production and the popularization of intensive crop planting, crop pests have become one of the important factors affecting agricultural production efficiency and agricultural product quality. Traditional pest control methods mainly use chemical pesticides. Although this method can effectively suppress pests in the short term, long-term use will lead to problems such as pest resistance, ecological damage and agricultural product residues.

[0003] In the existing technologies, endophytes and functional plants, as two green control methods, have shown certain application prospects in pest control. However, the existing technologies have the following main problems: first, there are more independent studies on endophytes and functional plants, but less research on their synergistic effects, and they have failed to fully explore their comprehensive control potential under different environmental conditions; second, pest control methods often lack dynamic adjustment mechanisms and are difficult to adapt to the complex changes in the agricultural environment; third, existing control strategies mostly rely on experience judgment and lack accurate prediction and optimization methods based on big data and artificial intelligence technology. Summary of the invention

[0004] The present invention provides a comprehensive prevention and control method combining highly effective insect-resistant endophytes with functional plants.

[0005] The comprehensive prevention and control method of the combination of highly effective insect-resistant endophytes and functional plants includes the following steps:

[0006] S1, data collection: through the sensor network deployed in the farm, multi-source data is collected in real time, including environmental data, pest species, plant growth status, endophyte activity, and functional plant growth;

[0007] S2, data processing: preprocessing the collected multi-source data, performing data cleaning and standardization, and extracting features from the preprocessed multi-source data;

[0008] S3, analysis of the synergistic effect between endophytes and functional plants: Based on the extracted features, the relationship between environmental conditions, endophytes and functional plants was analyzed through regression models, the impact of each factor on the pest control effect was quantified, and a synergistic effect prediction model was constructed using the support vector machine algorithm to predict the synergistic effect between endophytes and functional plants under different environmental conditions;

[0009] S4, generation of control strategies: based on the results of the synergistic effect analysis between endophytes and functional plants, generate personalized pest control strategies for the farm environment;

[0010] S5, Decision support: Provide decision support to farm managers based on the generated pest control strategies.

[0011] S6, Visualization display: Display analysis results and pest control strategies through a visualization interface.

[0012] Optionally, the S1 includes:

[0013] S11, Environmental data collection: By evenly deploying an environmental sensor network across the farm area, real-time data on temperature, humidity, light intensity, and precipitation are collected;

[0014] S12, pest species collection: collect the species and distribution of different pests through intelligent pest trapping devices and image recognition technology;

[0015] S13, plant growth status collection: monitoring plant growth status through multi-spectral imaging equipment and ultrasonic plant measuring instrument;

[0016] S14, Endophyte Activity Collection: Evaluate the quantity, type and activity of endophytes in the soil through sampling and molecular biological detection.

[0017] S15, collection of growth conditions of functional plants: monitoring the growth cycle, yield and resistance response of functional plants through sensor networks and intelligent analysis equipment deployed around the functional plants.

[0018] Optionally, S2 includes:

[0019] S21, data cleaning: clean the collected multi-source data and remove outliers and missing values;

[0020] S22, Data standardization: Data from different sources and units are processed uniformly to ensure data comparability.

[0021] S23, feature extraction: feature extraction is performed based on the cleaned and standardized multi-source data.

[0022] Optionally, in S3, based on the extracted features, the relationship between environmental conditions, endophytes and functional plants is analyzed by regression model to quantify the influence of each factor on the pest control effect, including:

[0023] S31, variable selection and definition: select variables related to pest control effects from the extracted features;

[0024] S32, taking the pest control effect as the dependent variable and the selected variables related to the pest control effect as the independent variables, a multivariate nonlinear regression model was constructed;

[0025] S33, Result analysis and quantitative contribution: Based on the multivariate nonlinear regression coefficients and the importance index of variables, the contribution of each factor to the pest control effect is quantified.

[0026] Optionally, the support vector machine algorithm is used to construct a mathematical model in S3 to predict the synergistic effect of endophytes and functional plants under different environmental conditions, including:

[0027] S34, data preparation: receiving the features extracted in S23, screening out the key features that have a significant impact on the synergistic effect, and integrating the screened key features to form a feature data set;

[0028] S35, data set division: the feature data set and the corresponding target variables are divided into a training set and a validation set, which are used for model training and evaluation respectively;

[0029] S36, model construction: construct a synergistic effect prediction model based on the support vector machine algorithm;

[0030] S37, model training and validation: The synergy effect prediction model was trained using the support vector regression algorithm in the training set, and the prediction performance of the model was evaluated on the validation set;

[0031] S38, synergistic effect prediction: Use the trained synergistic effect prediction model to predict the synergistic effect under different environmental conditions.

[0032] Optionally, the S4 includes:

[0033] S41, receiving data: obtaining synergistic effect analysis results and environmental characteristic data;

[0034] S42, Analyze farm environment and planting conditions: Analyze environmental characteristic data based on the current farm crop type, planting cycle and growth status, and identify key conditions that affect pest control (e.g., drought or high humidity environments may affect endophyte activity);

[0035] S43, Endophyte administration plan: Generate an endophyte administration plan, including administration time, administration dosage and administration method.

[0036] S44, Functional plant planting and configuration plan: Generate functional plant planting and configuration plan, including planting density, planting layout and species selection;

[0037] S45, generate control strategy output: generate pest control strategy based on endophyte application plan and functional plant planting and configuration plan.

[0038] Optionally, the S5 includes:

[0039] S51, decision output of the prevention and control strategy: providing the generated pest control strategy to farm managers.

[0040] S52, Dynamically adjust decision generation: Dynamically optimize prevention and control strategies based on multi-source data monitored in real time.

[0041] Optionally, the S6 includes:

[0042] S61, display of synergistic effect analysis results: display the analysis results of synergistic effects between endophytes and functional plants through a visual interface.

[0043] S62, prevention and control strategy display: Display the generated pest control strategy through a visual interface.

[0044] S63, display of control effect: display the changing trend of pest control rate through line graph or bar graph.

[0045] Beneficial effects of the present invention:

[0046] The present invention, through the synergistic effect analysis of endophytes and functional plants, quantifies the combined effects of environmental factors, endophyte activity and the insect-proof properties of functional plants, and accurately predicts the optimal control combination under different environmental conditions. The synergistic effect prediction model constructed based on the support vector machine algorithm can not only accurately evaluate the current control effect, but also dynamically predict the impact of environmental changes on pest control, realizing a scientific and accurate endophyte application and functional plant configuration plan. Compared with traditional pest control methods that rely on experience, the present invention greatly improves the control efficiency and reliability.

[0047] In the present invention, the sensor network collects multi-source data such as farm environmental data, endophyte activity, functional plant growth status, and pest distribution in real time, and uses feature extraction and data fusion technology to form a comprehensive prevention and control analysis basis. Based on these data, the prevention and control strategy can be adjusted in real time during the execution process, such as dynamically adjusting the application time and dosage of endophytes according to sudden rainfall or peak pest activity, or optimizing the functional plant planting plan. This real-time monitoring and dynamic optimization capability not only improves the sustainability of pest control, but also significantly reduces resource waste and environmental risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 The figure is a schematic diagram of a method flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0051] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0052] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0053] like Figure 1 As shown, the comprehensive prevention and control method of the combination of highly effective insect-resistant endophytes and functional plants includes the following steps:

[0054] S1, data collection: through the sensor network deployed in the farm, multi-source data is collected in real time, including environmental data, pest species, plant growth status, endophyte activity, and functional plant growth;

[0055] S2, data processing: preprocessing the collected multi-source data, performing data cleaning and standardization, and extracting features from the preprocessed multi-source data;

[0056] S3, analysis of the synergistic effect between endophytes and functional plants: Based on the extracted features, the relationship between environmental conditions, endophytes and functional plants was analyzed through regression models, the impact of each factor on the pest control effect was quantified, and a synergistic effect prediction model was constructed using the support vector machine algorithm to predict the synergistic effect between endophytes and functional plants under different environmental conditions;

[0057] S4, generation of control strategies: based on the results of the synergistic effect analysis between endophytes and functional plants, generate personalized pest control strategies for the farm environment;

[0058] S5, Decision support: Provide decision support to farm managers based on the generated pest control strategies.

[0059] S6, Visualization display: Display analysis results and pest control strategies through a visualization interface.

[0060] S1 includes:

[0061] S11, Environmental data collection: By evenly deploying an environmental sensor network across the farm area, real-time data on temperature, humidity, light intensity, and precipitation are collected, including:

[0062] The temperature and humidity sensors use high-precision digital sensors that can record dynamic changes in ambient temperature and humidity;

[0063] The light intensity is monitored in real time by a photometer to obtain the light energy distribution required for crop photosynthesis;

[0064] The precipitation is measured by rain sensors to obtain real-time precipitation data of farmland, providing a basis for crop growth environment analysis;

[0065] S12, pest species collection: collect the species and distribution of different pests through intelligent pest trapping devices and image recognition technology, including:

[0066] Use smart traps (such as sex-attractant traps or light traps) to capture pests;

[0067] Capture images of captured pests and classify them using image recognition technology to identify the types and frequency of occurrence of pests;

[0068] By setting up pest trapping devices distributed in different areas, the distribution of pests can be monitored in real time to form a pest distribution map;

[0069] S13, plant growth status collection: Monitor plant growth status through multi-spectral imaging equipment and ultrasonic plant measuring instrument, including:

[0070] Leaf color is monitored by multispectral imaging equipment, and relevant parameters such as chlorophyll content are extracted to assess the growth and health of plants;

[0071] Plant height is automatically measured by an ultrasonic plant measuring instrument, and crop growth is recorded regularly;

[0072] S14, Endophyte Activity Collection: Evaluate the quantity, type and activity of endophytes in the soil through sampling and molecular biological detection methods, including:

[0073] Regularly extract soil samples from designated farmland areas using an automated soil sampling device;

[0074] Use high-throughput sequencing technology or fluorescent quantitative PCR technology to analyze the types and relative abundance of endophytes in the soil;

[0075] The activity of endophytes was evaluated in combination with soil chemical analysis (such as pH value and nutrient content), providing basic data for subsequent synergistic effect analysis.

[0076] S15, functional plant growth status collection: through the sensor network and intelligent analysis equipment deployed around the functional plants, the growth cycle, yield and resistance response of the functional plants are monitored, including:

[0077] Use optical imaging equipment to monitor the growth cycle and epigenetic changes of functional plants;

[0078] Real-time monitoring of functional plant yield data through an intelligent weighing platform;

[0079] Biochemical detection equipment is used to monitor the content of secondary metabolites (such as terpenoids and flavonoids) in functional plants to evaluate their insect resistance.

[0080] S2 includes:

[0081] S21, data cleaning: Clean the collected multi-source data to remove abnormal values ​​and missing values, including:

[0082] Outlier detection: Use statistical methods (such as the 3σ principle) to detect outliers in the data and remove or correct them;

[0083] Missing value processing: For missing data, different processing methods are used according to the data type:

[0084] Environmental data (such as temperature, humidity, etc.) are filled in using linear interpolation;

[0085] For pest species data, the k-nearest neighbor algorithm is used to infer and fill in data based on similar samples;

[0086] For endophyte activity data, a generative adversarial network-based method was used to generate reasonable missing value supplements;

[0087] S22, Data Standardization: Data from different sources and units are processed uniformly to ensure data comparability, including:

[0088] Normalization: normalize numerical data such as environmental data and plant growth status to the interval [0,1] to eliminate dimension differences;

[0089] Category coding: One-hot encoding is used to process categorical data such as pest species to facilitate subsequent model analysis;

[0090] Time series processing: Time series segmentation and smoothing of dynamic data with time attributes (such as precipitation changes and pest species distribution) are performed to provide continuity features for subsequent analysis.

[0091] S23, feature extraction: Feature extraction is performed based on the cleaned and standardized multi-source data, including:

[0092] Environmental data feature extraction: calculate the mean, variance and extreme values ​​of environmental factors (such as daily average temperature and daily maximum humidity), and extract the periodic characteristics of environmental factors (such as the daily periodic characteristics of light changes);

[0093] Pest species data feature extraction: Based on pest distribution data, calculate the density characteristics and spatial distribution characteristics of pest species, and use principal component analysis to extract the main distribution areas of pests;

[0094] Plant growth status feature extraction: Extract pigment content-related features (such as chlorophyll concentration) from leaf color data, and extract growth rate and growth trend features based on plant height time series data;

[0095] Extraction of activity characteristics of endophytes: Analyze the concentration changes of endophyte metabolites, extract activity intensity characteristics, and calculate the diversity index (such as Shannon index and Simpson index) based on the endophyte species data;

[0096] Extraction of growth characteristics of functional plants: Extraction of dynamic change characteristics of yield of functional plants (such as yield increase rate), extraction of content change characteristics of insect-resistant secondary metabolites (such as temporal distribution of terpenoids).

[0097] In S3, based on the extracted features, the relationship between environmental conditions, endophytes and functional plants is analyzed through regression models to quantify the impact of each factor on the pest control effect, including:

[0098] S31, variable selection and definition: Variables related to pest control effects are selected from the extracted features, including:

[0099] Environmental factors: temperature, humidity, light intensity and precipitation;

[0100] Endophyte characteristics: number of endophyte species and activity of metabolites;

[0101] Functional plant characteristics: planting density and secondary metabolite concentrations of functional plants;

[0102] S32, taking the pest control effect as the dependent variable and the selected variables related to the pest control effect as the independent variables, a multivariate nonlinear regression model was constructed, specifically including:

[0103] Assuming that there is a nonlinear relationship between the dependent variable (pest control effect) and the independent variable, the form of the multivariate nonlinear regression model is expressed as:

[0104]

[0105] Among them, Y is the pest control effect (such as pest control rate or pest population density reduction rate), X1 is temperature, X2 is humidity, X3 is light intensity, X4 is precipitation, X5 is the number of endophyte species, X6 is the activity of endophyte metabolites, X7 is the concentration of functional plant secondary metabolites, X8 is the planting density of functional plants, β0, β1, ..., β6 are regression coefficients, indicating the influence weight of each variable on Y, ∈ is the error term, reflecting the random error of the model;

[0106] Model parameter estimation: Use the gradient descent method to optimize the model parameters and minimize the loss function (such as mean square error, MSE), expressed as:

[0107]

[0108] Among them, Y i is the observed value, is the model prediction value, n is the number of samples;

[0109] S33, Result analysis and quantitative contribution: Based on the multivariate nonlinear regression coefficient and the importance index of the variables, the contribution of each factor to the pest control effect is quantified, including:

[0110] Calculate the influence of temperature and humidity on the activity of endophytes;

[0111] To analyze the synergistic effects of secondary metabolite content and planting density on pest control rate;

[0112] Assess the sensitivity of metabolite activity to different environmental conditions.

[0113] Through the analysis method based on the multivariate nonlinear regression model, the quantitative contribution of environmental factors, endophyte characteristics, and functional plant characteristics to the pest control effect can be clarified, laying the foundation for the subsequent prediction of synergistic effects and the generation of control strategies.

[0114] In S3, a mathematical model was constructed using the support vector machine algorithm to predict the synergistic effects of endophytes and functional plants under different environmental conditions, including:

[0115] S34, data preparation: Receive the features extracted in S23, screen out the key features that have a significant impact on the synergistic effect, and integrate the screened key features to form a feature data set. The key features include:

[0116] Environmental characteristics: temperature, humidity, light intensity, precipitation;

[0117] Endophyte characteristics: number of species, activity of metabolites;

[0118] Functional plant characteristics: secondary metabolite concentration, planting density;

[0119] The pest control effect is used as the target variable (label data);

[0120] S35, data set division: the feature data set and the corresponding target variables are divided into a training set and a validation set, which are used for model training and evaluation respectively;

[0121] S36, model construction: Based on the support vector machine algorithm, a synergy effect prediction model is constructed, the core of which is:

[0122] Objective function: By optimizing the loss function Minimize errors;

[0123] Kernel function selection: Radial basis function (RBF kernel) is used to map the nonlinear feature space. The formula is:

[0124] K(x i ,x j )=exp(-γ||x i -x j || 2 );

[0125] Among them, γ is the kernel parameter, which controls the complexity of feature mapping;

[0126] S37, model training and validation: The synergy effect prediction model was trained using the support vector regression algorithm in the training set, and the prediction performance of the model was evaluated on the validation set;

[0127] S38, synergistic effect prediction: Use the trained synergistic effect prediction model to predict the synergistic effect under different environmental conditions, including:

[0128] Input variables were environmental characteristics, endophyte properties, and functional plant properties;

[0129] The output variable is the predicted value of pest control effect (such as control rate, population density reduction, etc.);

[0130] The prediction results were combined to generate synergistic effect curves among environmental characteristics, endophytes and functional plants, which were used to analyze the optimization strategies under different conditions.

[0131] S4 includes:

[0132] S41, receiving data: obtaining synergistic effect analysis results and environmental characteristic data;

[0133] The synergy analysis results include:

[0134] The best synergistic combination of endophytes and functional plants under different environmental conditions;

[0135] The predicted value of the synergistic effect on the pest control effect (such as pest control rate);

[0136] Environmental characteristic data include temperature, humidity, light intensity, and precipitation;

[0137] S42, Analyze farm environment and planting conditions: Analyze environmental characteristic data based on the current farm crop type, planting cycle and growth status, identify key conditions that affect pest control (such as drought or high humidity environment may affect the activity of endophytes), and obtain pest species and distribution;

[0138] S43, Endophyte Application Plan: Generate an endophyte application plan, including application time, application dosage and application method, specifically including:

[0139] S431, Application time: Determine the best time to apply endophytes based on pest activity patterns and environmental conditions, for example:

[0140] Apply before the peak pest outbreak period or during the critical growth period of crops (such as the seedling stage);

[0141] Avoid application under high temperature or strong light conditions to improve the survival rate and activity of endophytes.

[0142] S432, Application dosage: Based on the activity and synergistic effect analysis results of endophytic bacterial metabolites, the optimal application concentration is determined through dosage optimization calculation.

[0143] S433, Application method: Select the application method according to the needs of the target area, for example:

[0144] Spraying: used to control pests on the surface of plant leaves;

[0145] Rhizosphere injection: Increase the concentration of endophytes in the rhizosphere soil and enhance the protective ability of plant roots.

[0146] S44, Functional Plant Planting and Configuration Plan: Generate functional plant planting and configuration plan, including planting density, planting layout and species selection, including:

[0147] S441, Planting density: Based on the results of synergistic effect analysis, optimize the planting density of functional plants. For example, low-density planting may not effectively control pests, while too high a density may increase planting costs.

[0148] S442, Planting layout: Combine pest distribution characteristics with crop layout to design optimal planting locations for functional plants, form natural barriers at field boundaries, and plant in interval strips between crop rows to increase coverage and synergy.

[0149] S443, species selection: functional plant species are selected based on climate adaptability and insect resistance;

[0150] S45, generate control strategy output: generate pest control strategy based on endophyte application plan and functional plant planting and configuration plan.

[0151] S5 includes:

[0152] S51, decision output of control strategy: Provide the generated pest control strategy to farm managers, including:

[0153] Endophyte application plan: specific application time, dosage, method and expected control effect;

[0154] Functional plant configuration plan: planting density, layout location, preferred species and maintenance recommendations.

[0155] S52, Dynamically adjust decision generation: Dynamically optimize the prevention and control strategy based on real-time monitoring of multi-source data, including:

[0156] Adjust the frequency, dosage and method of endophyte application to suit the current environment;

[0157] Update functional plant planting or maintenance strategies (e.g., replanting, density adjustment).

[0158] Generate new optimization solutions by analyzing real-time multi-source data, such as:

[0159] After continuous rainfall, supplementary endophyte application is applied to ensure control effectiveness;

[0160] When pest migration trends intensify, increase the planting density of functional plants or expand the coverage area.

[0161] S6 includes:

[0162] S61, Synergistic effect analysis results display: The results of the synergistic effect analysis between endophytes and functional plants are displayed through a visual interface, including:

[0163] The best combination of endophytes and functional plants under current environmental conditions;

[0164] Predicted value of synergistic effect on pest control rate.

[0165] S62, prevention and control strategy display: Display the generated pest control strategy through a visual interface, including:

[0166] the timing, dosage and method of application of endophytes;

[0167] Planting density, planting location and species selection of functional plants.

[0168] S63, display of prevention and control effects: display the changing trend of pest control rate through line graph or bar graph.

[0169] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0170] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A comprehensive prevention and control method combining highly effective insect-resistant endophytes and functional plants, characterized in that: The following steps are involved: S1, data collection: through the sensor network deployed in the farm, multi-source data is collected in real time, including environmental data, pest species, plant growth status, endophyte activity, and functional plant growth; S2, data processing: preprocessing the collected multi-source data, performing data cleaning and standardization, and extracting features from the preprocessed multi-source data; S3, analysis of the synergistic effect between endophytes and functional plants: Based on the extracted features, the relationship between environmental conditions, endophytes and functional plants was analyzed through regression models, the impact of each factor on the pest control effect was quantified, and a synergistic effect prediction model was constructed using the support vector machine algorithm to predict the synergistic effect between endophytes and functional plants under different environmental conditions; S4, generation of control strategies: based on the results of the synergistic effect analysis between endophytes and functional plants, generate personalized pest control strategies for the farm environment; S5, decision support: providing decision support to farm managers based on the generated pest control strategies; S6, Visualization display: Display analysis results and pest control strategies through a visualization interface.

2. The comprehensive prevention and control method of the combination of highly effective insect-resistant endophytes and functional plants according to claim 1, characterized in that: The S1 includes: S11, Environmental data collection: By evenly deploying an environmental sensor network across the farm area, real-time data on temperature, humidity, light intensity, and precipitation are collected; S12, pest species collection: collect the species and distribution of different pests through intelligent pest trapping devices and image recognition technology; S13, plant growth status collection: monitoring plant growth status through multi-spectral imaging equipment and ultrasonic plant measuring instrument; S14, Endophyte Activity Collection: Evaluate the quantity, type and activity of endophytes in soil through sampling and molecular biological detection; S15, collection of growth conditions of functional plants: monitoring the growth cycle, yield and resistance response of functional plants through sensor networks and intelligent analysis equipment deployed around the functional plants.

3. The comprehensive prevention and control method of the combination of highly effective insect-resistant endophytes and functional plants according to claim 2, characterized in that: The S2 includes: S21, data cleaning: clean the collected multi-source data and remove outliers and missing values; S22, data standardization: data from different sources and units are processed uniformly to ensure data comparability; S23, feature extraction: feature extraction is performed based on the cleaned and standardized multi-source data.

4. The comprehensive prevention and control method of the combination of highly effective insect-resistant endophytes and functional plants according to claim 3, characterized in that: In S3, based on the extracted features, the relationship between environmental conditions, endophytes and functional plants is analyzed through regression models to quantify the impact of each factor on the pest control effect, including: S31, variable selection and definition: select variables related to pest control effects from the extracted features; S32, taking the pest control effect as the dependent variable and the selected variables related to the pest control effect as the independent variables, a multivariate nonlinear regression model was constructed; S33, Result analysis and quantitative contribution: Based on the multivariate nonlinear regression coefficients and the importance index of variables, the contribution of each factor to the pest control effect is quantified.

5. The comprehensive prevention and control method of the combination of highly effective insect-resistant endophytes and functional plants according to claim 4, characterized in that: In S3, a mathematical model is constructed using a support vector machine algorithm to predict the synergistic effects of endophytes and functional plants under different environmental conditions, including: S34, data preparation: receiving the features extracted in S23, screening out the key features that have a significant impact on the synergistic effect, and integrating the screened key features to form a feature data set; S35, data set division: the feature data set and the corresponding target variables are divided into a training set and a validation set, which are used for model training and evaluation respectively; S36, model construction: construct a synergistic effect prediction model based on the support vector machine algorithm; S37, model training and validation: The synergy effect prediction model was trained using the support vector regression algorithm in the training set, and the prediction performance of the model was evaluated on the validation set; S38, synergistic effect prediction: Use the trained synergistic effect prediction model to predict the synergistic effect under different environmental conditions.

6. The comprehensive prevention and control method of the combination of highly effective insect-resistant endophytes and functional plants according to claim 5, characterized in that: The S4 includes: S41, receiving data: obtaining synergistic effect analysis results and environmental characteristic data; S42, Analyze farm environment and planting conditions: Analyze environmental characteristic data based on the current crop type, planting cycle and growth status of the farm to identify key conditions affecting pest control; S43, endophyte application plan: generating an endophyte application plan, including application time, application dosage and application method; S44, Functional plant planting and configuration plan: Generate functional plant planting and configuration plan, including planting density, planting layout and species selection; S45, generate control strategy output: generate pest control strategy based on endophyte application plan and functional plant planting and configuration plan.

7. The comprehensive prevention and control method of the combination of highly effective insect-resistant endophytes and functional plants according to claim 6, characterized in that: The S5 includes: S51, decision output of control strategy: providing the generated pest control strategy to farm managers; S52, Dynamically adjust decision generation: Dynamically optimize prevention and control strategies based on multi-source data monitored in real time.

8. The comprehensive prevention and control method of the combination of highly effective insect-resistant endophytes and functional plants according to claim 7, characterized in that: The S6 includes: S61, display of synergistic effect analysis results: display the analysis results of synergistic effects between endophytes and functional plants through a visual interface; S62, prevention and control strategy display: display the generated pest control strategy through a visual interface; S63, display of control effect: display the changing trend of pest control rate through line graph or bar graph.

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