Tea garden green prevention and control method based on plant repelling zone

Abstract

The invention discloses a tea garden green prevention and control method based on a repellent plant zone, and belongs to the technical field of tea garden management, and the method comprises the following steps: S1, collecting the field environment data of a target tea garden, synchronously collecting the attribute data of various candidate repellent plants, and forming a tea garden planning basic data set; s2, based on the tea garden planning basic data set, constructing a virtual planning space, and generating a preliminary repellent plant configuration scheme; s3, in the virtual planning space, simulating an expected effect of the preliminary repellent plant configuration scheme in a preset period, and outputting an optimized repellent plant configuration scheme; s4, according to the optimized repellent plant configuration scheme, high-quality seedlings are cultivated in a solid nursery garden; and S5, executing the optimized repellent plant configuration scheme in the target tea garden. According to the method, the virtual planning space is constructed, the repellent plant configuration scheme accurately matched with the pest and disease damage risk is generated, and the adaptability of the variety, the number and the space distribution to the actual prevention and control requirement is improved.

Description

Technical Field

[0001] This invention relates to the field of tea garden management technology, and in particular to a green pest control method for tea gardens based on repellent plant strips. Background Technology

[0002] Green pest control in tea gardens refers to the use of biological means such as repelling plants and natural enemy insects to regulate pests and diseases in tea gardens by constructing an ecological balance system, reducing dependence on chemical pesticides, and aiming to ensure the long-term safety of tea yield, quality, and the ecological environment of tea gardens, and achieve sustainable management of pests and diseases.

[0003] Existing tea garden repellent plant configuration techniques mainly rely on manual experience. They are usually based on a rough survey of tea garden topography, soil type and common pests and diseases, and select a few plants known to have insect-repelling effects. They are then planted in simple strips or blocks at the boundaries of the tea garden or in specific areas. There is a lack of systematic analysis of the complex relationship between plant growth characteristics, insect-repelling efficacy and environmental factors. The configuration process does not incorporate quantitative models and dynamic simulations.

[0004] Existing tea garden repellent plant configuration technologies suffer from the following shortcomings: First, the configuration schemes lack precision and adaptability, making it difficult to conduct quantitative analysis and optimization calculations based on multi-source environmental data and plant attributes. This results in a low degree of matching between the types, quantities, and spatial distribution of repellent plants and actual pest and disease control needs. Second, the schemes lack a closed-loop optimization mechanism after implementation. They cannot provide dynamic feedback and scheme correction based on the actual growth status of the plants and the effectiveness of pest and disease control after planting, nor can they effectively link the optimized configuration schemes with the targeted seedling cultivation stage. This leads to a gradual decline in control effectiveness and low resource utilization efficiency. Therefore, there is an urgent need to provide a green pest control method for tea gardens based on repellent plant strips to solve these problems. Summary of the Invention

[0005] The technical problem to be solved by this invention is to overcome the following shortcomings of the prior art: First, the configuration scheme lacks precision and adaptability, making it difficult to conduct quantitative analysis and optimization calculations based on multi-source environmental data and plant attributes, resulting in a low degree of matching between the types, quantities, and spatial distribution of repellent plants and actual control needs; second, the scheme lacks a closed-loop optimization mechanism after implementation, making it impossible to dynamically provide feedback and scheme correction based on the actual growth status of the plants after planting and the effect of pest and disease control, and also failing to effectively link the optimized configuration scheme with the targeted seedling cultivation stage, resulting in a gradual decline in control effect and low resource utilization efficiency. This invention provides a green control method for tea gardens based on repellent plant strips.

[0006] To solve the above-mentioned technical problems, one technical solution adopted by the present invention is: to provide a green control method for tea gardens based on repellent plant strips, comprising the following steps: S1. Collect on-site environmental data of the target tea garden, and simultaneously collect the self-attribute data of various candidate repellent plants. Integrate the on-site environmental data and the self-attribute data to form a basic dataset for tea garden planning. S2. Based on the tea garden planning basic dataset, construct a virtual planning space, and within the virtual planning space, screen and spatially configure various candidate repellent plants to generate a preliminary repellent plant configuration scheme. S3. In the virtual planning space, simulate the expected effect of the preliminary repellent plant configuration scheme within a preset period; adjust the preliminary repellent plant configuration scheme according to the difference between the simulated expected effect and the preset optimization target until the difference reaches the predetermined standard, and output the optimized repellent plant configuration scheme. S4. Based on the optimized repellent plant configuration scheme, in a physical nursery, the target repellent plants in the optimized repellent plant configuration scheme are targeted for screening or breeding to cultivate high-quality seedlings that meet the requirements of the optimized repellent plant configuration scheme. S5. Implement the optimized repellent plant configuration scheme in the target tea garden, and complete the on-site planting of the repellent plants using the cultivated high-quality seedlings.

[0007] The present invention is further configured such that: the on-site environmental data in step S1 includes the topographic data, soil data and historical pest and disease data of the target tea garden; The intrinsic attribute data includes the growth characteristics and insect repellent efficacy data of the candidate repellent plants; The actual environmental data and the self-attribute data are collected through a preset data acquisition terminal and data interface.

[0008] The present invention is further configured such that the method for constructing the virtual planning space in step S2 is as follows: S201. Call the preset twin space model, map the terrain data in the actual environment data to the twin space model, generate a virtual terrain framework consistent with the target tea garden terrain, integrate the soil data in the actual environment data into the virtual terrain framework, establish virtual plots, divide the virtual plots into multiple virtual sub-plots according to the soil data, and attach a unique preset spatial identifier label to each virtual sub-plot. Match the historical pest and disease data in the actual environment data with the spatial range of the virtual sub-plots according to their recorded geographical locations, and bind the occurrence frequency and severity data in the successfully matched historical pest and disease data to the spatial identifier label of the matched virtual sub-plot. S202. Based on the frequency and severity data of occurrence in the completed virtual sub-plots and the historical pest and disease data they are bound to, and according to the growth characteristic data and pest-repelling efficacy data in the self-attribute data of the candidate repellent plants, an extraction ratio is generated. The basic quantity of each candidate repellent plant is selected according to the extraction ratio, and each candidate repellent plant is allocated to the virtual sub-plots according to the basic quantity to construct a virtual planning space.

[0009] The present invention is further configured such that the step of generating the extraction ratio in step S202 is as follows: S2021. Based on the frequency and severity data of the historical pest and disease data bound to the virtual sub-plot, calculate a unique pest and disease risk index for each virtual sub-plot. The frequency data quantifies the frequency of historical pest and disease events occurring on this virtual sub-plot within a preset historical period, and the severity data represents the intensity level of the impact of historical pest and disease events on the target tea garden. The frequency value and the intensity level value are weighted and fused according to a preset first weight to generate the pest and disease risk index of the virtual sub-plot. S2022. Based on the growth characteristic data and insect repellency data of the candidate repellent plants, the growth characteristic data and insect repellency data are merged according to a preset rule to generate a plant efficacy index for each candidate repellent plant. S2023. For each virtual sub-plot, compare the pest and disease risk index with the plant efficacy index of each candidate repellent plant to be allocated to the virtual sub-plot; if the pest and disease risk index of the virtual sub-plot is higher than a first preset risk threshold, then extract a first preset basic quantity ratio for the candidate repellent plants whose plant efficacy index is higher than the first preset threshold. If the pest and disease risk index of the virtual sub-plot is between the first preset risk threshold and the second preset risk threshold, then the second preset basic quantity extraction ratio is selected; if the pest and disease risk index of the virtual sub-plot is less than the first preset risk threshold, then the third preset basic quantity extraction ratio is allocated according to the plant efficacy index, and the specific extraction ratio of each candidate repellent plant in each virtual sub-plot is determined.

[0010] The present invention is further configured such that the method for generating the preliminary repellent plant configuration scheme in step S2 is as follows: S203. Based on the extraction ratio of each candidate repellent plant determined for each virtual sub-plot in the virtual planning space, calculate the initial configuration quantity of each candidate repellent plant in different virtual sub-plots, and generate an initial spatial distribution matrix of candidate repellent plants by combining the actual geographical location corresponding to the spatial identifier label of the virtual sub-plot. S204. Based on the initial spatial distribution matrix, analyze the potential ecological interaction effects when different candidate repellent plants are configured in the same or adjacent virtual sub-plots. For candidate repellent plant combinations with significant synergistic or cooperative repellent effects in the potential ecological interaction effects, increase the initial configuration quantity of each candidate repellent plant by a preset first gain ratio based on the initial configuration quantity. For candidate repellent plant combinations with significant antagonistic effects or resource competition exceeding a preset competition threshold in the potential ecological interaction effects, adjust the spatial relative position of each candidate repellent plant in the candidate repellent plant combination, or reduce the initial configuration quantity of each candidate repellent plant by a preset first inhibition ratio to form an optimized plant combination spatial distribution scheme. S205. The optimized plant combination spatial distribution scheme is matched with the pest and disease risk index bound to each virtual sub-plot in the virtual planning space. The specific verification method is as follows: the ratio of the comprehensive repellency efficiency index of the candidate repellent plant combination allocated to each virtual sub-plot to the pest and disease risk index of the virtual sub-plot is calculated. If the ratio is higher than the preset high matching threshold, the optimized plant combination spatial distribution scheme of the virtual sub-plot is determined to be at the preset high protection efficiency level. If the ratio is between the preset high matching threshold and the preset low matching threshold, the optimized plant combination spatial distribution scheme of the virtual sub-plot is determined to be at the preset medium protection efficiency level. If the ratio is lower than the preset low matching threshold, the optimized plant combination spatial distribution scheme of the virtual sub-plot is determined to be at the preset low protection efficiency level, and a preset priority adjustment procedure needs to be initiated. After verifying and adjusting all the virtual sub-plots, the output is a preliminary repellent plant configuration scheme.

[0011] The present invention is further configured such that: the specific steps of simulating the expected effect of the preliminary repellent plant configuration scheme within a preset period in the virtual planning space in step S3 are as follows: In the virtual planning space, the types, quantities, and spatial distribution data of the repellent plants defined by the preliminary repellent plant configuration scheme are loaded, and the time parameters of the preset period are set; based on the pest and disease risk index of the virtual sub-plot and the plant efficacy index of the configured repellent plants, the dynamic interaction process between the pest and disease population and the repellent plant community within the preset period is simulated, the dynamic interaction process including the migration and diffusion patterns of pests and diseases, the allelopathic release effect of repellent plants, and the attraction and colonization behavior of natural enemy insects; the pest and disease control efficiency, plant community biomass, and ecological balance index at the end of the preset period are obtained through simulation calculation as the expected effect.

[0012] The present invention is further configured such that the output method of the optimized repellent plant configuration scheme in step S3 is: S301. Based on the expected effects of pest and disease control efficiency, plant community biomass and ecological balance index at the end of the preset period obtained from the simulation, compare them item by item with the target control efficiency, target biomass and target ecological balance index in the preset optimization targets, calculate the achievement deviation value of each indicator, and merge the achievement deviation values ​​of each item according to the preset second weight to generate a comprehensive optimization urgency index, and determine the optimization priority sequence based on the comprehensive optimization urgency index. S302. For the highest priority indicator in the optimization priority sequence, based on the sign and magnitude of the realization deviation value, modify the type, quantity or spatial distribution data of the repellent plants in the preliminary repellent plant configuration scheme, and re-simulate the expected effect within the preset period based on the modified preliminary repellent plant configuration scheme to obtain the updated pest and disease control efficiency, plant community biomass and ecological balance index. S303. The updated indicators obtained from the re-simulation are iteratively compared with the preset optimization target. If the comprehensive optimization urgency index drops below the predetermined optimization threshold and all sub-indicators reach the preset range, the current preliminary repellent plant configuration scheme is determined to be an optimized convergence scheme, and the output is the optimized repellent plant configuration scheme.

[0013] The present invention is further configured such that: the specific content of step S4 is as follows: the target repellent plant species and their required specific resistance traits and allelochemical secretion capacity are determined according to the optimized repellent plant configuration scheme; a targeted screening standard is formulated based on the target repellent plant species and their required specific resistance traits and allelochemical secretion capacity; then, the targeted screening standard is applied to screen the existing preset germplasm resources in a physical nursery; individuals that meet the standard are subjected to targeted breeding using artificial pollination or molecular marker-assisted selection technology; during the breeding process, the specific resistance traits and allelochemical secretion capacity of the offspring repellent plants are continuously monitored; and high-quality seedlings that meet the requirements of the optimized repellent plant configuration scheme and have stable traits are cultivated.

[0014] The present invention is further configured such that step S5 includes: after the actual planting of the target tea garden is completed, continuously monitoring the actual occurrence of pests and diseases and the plant growth status of the target tea garden, and feeding back the data of the actual occurrence of pests and diseases and the plant growth status to the virtual planning space, performing real-time comparison and analysis with the expected effect simulated in the virtual planning space, calculating the deviation coefficient between the actual value and the simulated value, and if the deviation coefficient exceeds the preset dynamic adjustment threshold, automatically triggering the optimization iteration process of step S3, regenerating the optimized repellent plant configuration scheme, and adjusting the repellent plant strip planted in the actual area according to the updated optimized repellent plant configuration scheme.

[0015] The beneficial effects of this invention are as follows: 1. This invention constructs a virtual planning space, integrates multi-source environmental data and plant attributes for quantitative analysis, and generates a repellent plant configuration scheme that is precisely matched with the risk of pests and diseases, significantly improving the adaptability of species, quantity and spatial distribution to actual prevention and control needs; 2. This invention forms a closed-loop optimization mechanism by comparing the simulated expected effect with the preset optimization target and the deviation-driven dynamic adjustment, combined with the feedback of monitoring data after on-site planting, to ensure the continuous effectiveness of the prevention and control plan and its resistance to effect decay. 3. This invention directly links the optimized repellent plant configuration scheme with the targeted seedling cultivation process, and selects and cultivates high-quality seedlings according to the scheme requirements, thereby achieving efficient resource utilization and guaranteed results from virtual planning to actual planting. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart of the virtual planning space construction method of the present invention; Figure 3 This is a flowchart of the output method for the optimized repellent plant configuration scheme of the present invention. Detailed Implementation

[0017] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby providing a clearer and more explicit definition of the scope of protection of the present invention.

[0018] Please see Figure 1 - Figure 3 The green control method for tea gardens based on repellent vegetation zones includes the following steps: S1. Collect on-site environmental data of the target tea garden, and simultaneously collect the self-attribute data of various candidate repellent plants. Integrate the on-site environmental data and self-attribute data to form a basic dataset for tea garden planning. S2. Based on the basic dataset of tea garden planning, a virtual planning space is constructed. Within the virtual planning space, various candidate repellent plants are screened and spatial configuration calculations are performed to generate a preliminary repellent plant configuration scheme. S3. In the virtual planning space, simulate the expected effect of the preliminary repellent plant configuration scheme within a preset period; based on the difference between the simulated expected effect and the preset optimization target, adjust the preliminary repellent plant configuration scheme until the difference reaches the predetermined standard, and output the optimized repellent plant configuration scheme. S4. Based on the optimized repellent plant configuration scheme, in the physical nursery, the target repellent plants in the optimized repellent plant configuration scheme are targeted for screening or breeding to cultivate high-quality seedlings that meet the requirements of the optimized repellent plant configuration scheme. S5. Implement the optimized repellent plant configuration scheme in the target tea garden and complete the on-site planting of repellent plants using the cultivated high-quality seedlings.

[0019] Steps S1-S5 simulate and optimize the configuration scheme of repellent plants by constructing a virtual planning space, which significantly improves the scientificity and reliability of the prevention and control scheme; combined with targeted breeding technology to cultivate high-quality seedlings, ensuring that the efficacy of repellent plants is highly adapted to the tea garden environment, and finally achieving efficient and precise green prevention and control of tea garden pests and diseases, forming a closed loop of ecological management with full-process optimization.

[0020] Among them, the on-site environmental data in step S1 includes the topographic data, soil data and historical pest and disease data of the target tea garden; The intrinsic attribute data includes the growth characteristics and insect repellent efficacy data of the candidate repellent plants; Field environmental data and its own attribute data are collected through preset data acquisition terminals and data interfaces. Topographic data is obtained through UAV mapping and geographic information systems, soil data is obtained through sensors at preset monitoring points and laboratory analysis, historical pest and disease data is extracted from the tea garden management database, and plant growth characteristic data and insect repellent efficacy data are obtained through field observation records and literature database queries. The preset data acquisition terminals include UAVs, multi-parameter soil sensors, pest and disease monitoring cameras, and portable plant physiological measuring instruments. The data interface adopts the MQTT protocol interface that conforms to the agricultural Internet of Things standard, supporting real-time data transmission and historical data retrieval.

[0021] One embodiment of the present invention is as follows: The method for constructing the virtual planning space in step S2 is as follows: S201. Call the preset twin space model, map the terrain data in the actual environment data to the twin space model, generate a virtual terrain framework consistent with the target tea garden terrain, integrate the soil data in the actual environment data into the virtual terrain framework, establish virtual plots, divide the virtual plots into multiple virtual sub-plots according to the soil data, and attach a unique preset spatial identifier label to each virtual sub-plot. Match the historical pest and disease data in the actual environment data with the spatial range of the virtual sub-plots according to their recorded geographical location, and bind the occurrence frequency and severity data in the successfully matched historical pest and disease data to the spatial identifier label of the matched virtual sub-plot. The preset twin space model adopts a multi-layer fusion digital twin space model. This digital twin space model maps terrain data to a virtual frame and integrates soil data and historical pest and disease data in layers to form a virtual terrain frame with a unified spatial benchmark, supporting dynamic updates and multi-scale spatial analysis. Preset spatial identification label: A unique code assigned to each virtual sub-plot. Its structure includes geographic location coordinates, soil type code and historical pest and disease identifiers. It is used to associate the virtual sub-plot with the actual tea garden area and bind the frequency and severity indicators of occurrence in historical pest and disease data. S202. Based on the frequency and severity data of occurrence in the completed virtual sub-plots and their associated historical pest and disease data, and according to the growth characteristics and pest-repelling efficacy data in the self-attribute data of candidate repellent plants, an extraction ratio is generated. The basic quantity of each candidate repellent plant is selected according to the extraction ratio, and each candidate repellent plant is allocated to the virtual sub-plots according to the basic quantity to construct a virtual planning space.

[0022] In step S202, each candidate repellent plant needs to be converted into a virtual state and then assigned to a virtual sub-plot. The conversion steps are as follows: when each candidate repellent plant is converted into a virtual state, firstly, key parameters (such as suitable pH, shade tolerance, and release rate of repellent components) in its growth characteristic data and insect repellency data are extracted and encoded into standardized virtual plant units; then, through geometric modeling and attribute binding, virtual plant objects with spatial coordinates, growth cycle simulation capabilities, and ecological interaction attributes are generated, and finally matched with the soil and pest data of the virtual sub-plot.

[0023] The step of generating the extraction ratio in step S202 is as follows: S2021. Based on the frequency and severity data of historical pest and disease data bound to virtual sub-plots, calculate a unique pest and disease risk index for each virtual sub-plot. The frequency data quantifies how often historical pest and disease events occur on this virtual sub-plot within a preset historical period, and the severity data represents the intensity level of the impact of historical pest and disease events on the target tea garden. The frequency value and the intensity level value are weighted and merged according to a preset first weight to generate the pest and disease risk index of the virtual sub-plot. The preset first weight is used to weight and integrate historical data on the frequency and severity of pests and diseases. Its optimal value is 0.3 for frequency and 0.7 for severity. This ratio is adjusted based on the actual significance of the severity of historical disaster events on tea garden yield. S2022. Based on the growth characteristic data and insect repellency data of the candidate repellent plants, the growth characteristic data and insect repellency data are merged according to preset rules to generate a plant efficacy index for each candidate repellent plant. The growth characteristic data is used to evaluate the plant's adaptability to the soil properties represented by the soil data, and the insect repellency data is used to evaluate the plant's ability to suppress the types of pests and diseases recorded in the historical pest and disease data. The specific steps for merging and generating the plant efficacy index for each candidate repellent plant according to preset rules are as follows: First, the soil adaptability and growth rate in the growth characteristic data and the target pest inhibition rate and duration of action in the insect repellency data are normalized; then, the initial efficacy value is calculated by linear weighted fusion, with soil adaptability weighted at 0.4 and target pest inhibition rate weighted at 0.6; finally, environmental correction factors (such as regional climate matching degree) are introduced for fine-tuning, and the standardized plant efficacy index is output. S2023. For each virtual sub-plot, compare the pest and disease risk index with the plant efficacy index of each candidate repellent plant to be allocated to the virtual sub-plot; if the pest and disease risk index of the virtual sub-plot is higher than the first preset risk threshold, then extract the first preset basic quantity ratio of the candidate repellent plants whose plant efficacy index is higher than the first preset threshold. The first preset risk threshold is the critical value used to distinguish between high-risk and medium-risk virtual sub-plots. Its optimal value is 0.75, which is based on the statistical analysis of historical pest and disease events that have resulted in economic losses exceeding 15%. The first preset basic quantity extraction ratio: For high-efficiency plants (plant efficiency index ≥ 0.8) in high-risk virtual sub-plots, the optimal value is that the number of single-type plant configurations accounts for 40% of the total capacity of the sub-plot, so as to ensure the concentrated exertion of efficient repellency effect; If the pest and disease risk index of the virtual sub-plot is between the first preset risk threshold and the second preset risk threshold, then the second preset basic quantity extraction ratio is selected. The second preset basic quantity extraction ratio is a balanced distribution, with emphasis on repellent plants with higher plant efficacy index. If the pest and disease risk index of the virtual sub-plot is less than the first preset risk threshold, then the third preset basic quantity extraction ratio is allocated according to the plant efficacy index, and plant diversity considerations can be appropriately introduced to determine the specific extraction ratio of each candidate repellent plant in each virtual sub-plot.

[0024] The second preset risk threshold is a critical value used to distinguish between medium-risk and low-risk virtual sub-plots. Its optimal value is 0.45, which is obtained by balancing the risk distribution and plant efficacy requirements through cluster analysis. The second preset basic quantity extraction ratio: For medium-risk virtual sub-plots, a balanced allocation strategy is adopted, with the optimal value being that the quantity of each type of plant configuration accounts for 15%-25% of the total capacity of the sub-plot, and priority is given to allocating plant species with higher efficiency indices.

[0025] The method for generating the preliminary repellent plant configuration scheme in step S2 is as follows: S203. Based on the extraction ratio of each candidate repellent plant determined for each virtual sub-plot in the virtual planning space, calculate the initial configuration quantity of each candidate repellent plant in different virtual sub-plots, and generate the initial spatial distribution matrix of candidate repellent plants by combining the actual geographical location corresponding to the spatial identifier label of the virtual sub-plot. The initial configuration quantity is calculated as follows: divide the total plantable area of ​​the virtual sub-plot by the standard area occupied by a single plant to obtain the theoretical maximum capacity; then multiply by the specific extraction ratio of the candidate repellent plant in the virtual sub-plot and take the integer value to finally obtain the initial configuration quantity of each candidate repellent plant. The steps for generating the initial spatial distribution matrix are as follows: using the spatial identifier labels of virtual sub-plots as row indices and the candidate repellent plant species as column indices, fill the calculated initial configuration quantity of each plant in each virtual sub-plot into the corresponding position in the matrix; then, based on the actual geographical coordinates of the virtual sub-plots, generate a two-dimensional distribution matrix containing spatial relationships. S204. Based on the initial spatial distribution matrix, analyze the potential ecological interaction effects when different candidate repellent plants are configured in the same or adjacent virtual sub-plots. The ecological interaction effects include allelopathic interactions among candidate repellent plants, i.e., synergistic or antagonistic effects, synergistic repellency or attraction effects against pests and diseases, and the degree of competition for light, heat, water, fertilizer, and environmental resources. For candidate repellent plant combinations with significant synergistic or synergistic repellency effects in the potential ecological interaction effects, increase the initial configuration quantity of each candidate repellent plant according to a preset first gain ratio based on the initial configuration quantity. For candidate repellent plant combinations with significant antagonistic effects or resource competition exceeding a preset competition threshold in the potential ecological interaction effects, adjust the spatial relative position of each candidate repellent plant in the candidate repellent plant combination. The adjustment method is such as increasing the interval distance or re-planting in different zones, or reducing the initial configuration quantity of each candidate repellent plant according to a preset first inhibition ratio, to form an optimized plant combination spatial distribution scheme. The preset first gain ratio: for plant combinations with synergistic repulsion effects, its optimal value is to increase the number of plants by 20% based on the initial configuration. Preset competition threshold: A critical value used to judge the degree of resource competition, with the optimal value being that the overlap of light, heat, water and fertilizer demand exceeds 60%; The preset first inhibition ratio: For plant combinations with antagonistic effects, its optimal value is 30% less than the initial configuration quantity; S205. Verify the matching degree between the optimized plant combination spatial distribution scheme and the pest and disease risk index bound to each virtual sub-plot in the virtual planning space. The specific verification method is as follows: Calculate the ratio of the comprehensive repellency efficiency index of the candidate repellent plant combination assigned to each virtual sub-plot to the pest and disease risk index of the virtual sub-plot. If the ratio is higher than the preset high matching threshold, the optimized plant combination spatial distribution scheme of the virtual sub-plot is determined to be at the preset high protection efficiency level; if the ratio is between the preset high matching threshold and the preset low matching threshold, the optimized plant combination spatial distribution scheme of the virtual sub-plot is determined to be at the preset medium protection efficiency level; if the ratio is lower than the preset low matching threshold, the optimized plant combination spatial distribution scheme of the virtual sub-plot is determined to be at the preset low protection efficiency level, and activation is required. The preset priority adjustment procedure is as follows: First, the currently configured repellent plants are sorted from high to low according to their plant efficacy index, and the top two repellent plants are retained as the core combination. Second, repellent plants with insect repellent efficacy data values ​​higher than the preset efficacy threshold against the main pests and diseases of the current virtual sub-plot and without antagonistic effects with the core combination are selected from the preset candidate plant library as supplementary plants. Next, the protection efficacy gap that needs to be improved is calculated, and the number of high repellent plants in the core combination is increased according to the preset second gain ratio based on the protection efficacy gap, and 1 to 2 supplementary plants are introduced, with their initial configuration quantity allocated according to their plant efficacy index. Finally, the ratio of the comprehensive repellent efficacy index to the pest and disease risk index of the adjusted repellent plant combination is recalculated to ensure that it reaches or exceeds the low matching threshold. The preset high matching threshold is: the optimal value is that the ratio of the comprehensive repellency effectiveness index to the pest and disease risk index is not less than 1.2; The preset low matching threshold is: the optimal value is that the ratio of the comprehensive repellency effectiveness index to the pest and disease risk index is not less than 0.8; The calculation steps for the protection efficiency gap that needs to be improved are as follows: subtract the actual ratio of the comprehensive repellency efficiency index of the current plant combination to the pest and disease risk index from the preset low matching threshold. The difference obtained is the protection efficiency gap that needs to be improved. The comprehensive repellency efficacy index is calculated by weighting the insect repellency efficacy data of each repellent plant in the combination and the number of plants configured. The specific calculation method is as follows: multiply the insect repellency efficacy data of each repellent plant in the plant combination by the number of plants configured to obtain the contribution value of that plant; sum the contribution values ​​of all plants and divide them by the total number of plants configured in the plant combination to obtain the weighted average repellency efficacy index. After verifying and adjusting all virtual sub-plots, the output is a preliminary repellent plant configuration scheme, which clearly specifies the types and quantities of repellent plants and their specific distribution locations in the virtual space of the tea garden.

[0026] This embodiment achieves dynamic optimization and precise spatial matching of repellent plant configuration by constructing a precise virtual planning space and quantitative evaluation system, which significantly improves the pertinence and ecological compatibility of pest and disease control in tea gardens. At the same time, it reduces the cost of manual intervention through parameterized models and forms a reusable green prevention and control technology paradigm.

[0027] One embodiment of the present invention is as follows: the specific steps of simulating the expected effect of the preliminary repellent plant configuration scheme within a preset period in the virtual planning space in step S3 are as follows: in the virtual planning space, the types, quantities and spatial distribution data of repellent plants defined by the preliminary repellent plant configuration scheme are loaded, and the time parameters of the preset period are set; based on the pest and disease risk index of the virtual sub-plot and the plant efficacy index of the configured repellent plants, the dynamic interaction process between the pest and disease population and the repellent plant community within the preset period is simulated. The dynamic interaction process includes the migration and diffusion patterns of pests and diseases, the allelopathic release effect of repellent plants, and the attraction and colonization behavior of natural enemy insects; the pest and disease control efficiency, plant community biomass and ecological balance index at the end of the preset period are obtained through simulation calculation as the expected effect.

[0028] Preset period: The optimal value is 12 months. This period covers the entire growing season of tea trees and the annual occurrence pattern of pests and diseases, ensuring that the simulation can fully reflect seasonal fluctuations and long-term ecological effects. The setting is based on the alignment analysis of tea tree phenological data and historical peak periods of pests and diseases. The calculation of pest and disease control efficiency, plant community biomass, and ecological balance index was carried out by: pest and disease control efficiency was calculated by accumulating the rate of reduction of pest and disease population per unit time using the integral method; plant community biomass was estimated by the photosynthesis-respiration balance model; and ecological balance index was generated by weighted fusion of Shannon diversity index and network interaction intensity. All three types of indicators were dimensionless to eliminate the influence of dimensions.

[0029] The optimized plant repellency configuration scheme in step S3 is output as follows: S301. Based on the expected effects of pest and disease control efficiency, plant community biomass and ecological balance index at the end of the preset period obtained from the simulation, compare them item by item with the target control efficiency, target biomass and target ecological balance index in the preset optimization targets, calculate the achievement deviation value of each indicator, and merge the achievement deviation values ​​according to the preset second weight to generate a comprehensive optimization urgency index, and determine the optimization priority sequence based on the comprehensive optimization urgency index. The second preset weight is allocated according to the contribution of each indicator to the stability of the system. The weight of pest and disease control efficiency is set to 0.5, the weight of plant community biomass is set to 0.3, and the weight of ecological balance index is set to 0.2. The principle of weight allocation is to prioritize the effectiveness of prevention and control while maintaining ecological sustainability. S302. For the highest priority indicator in the optimization priority sequence, modify the type, quantity, or spatial distribution data of repellent plants in the preliminary repellent plant configuration scheme according to the sign and magnitude of the achievement deviation value. Based on the modified preliminary repellent plant configuration scheme, re-simulate the expected effect within the preset period to obtain the updated pest and disease control efficiency, plant community biomass, and ecological balance index. For example, for virtual sub-plots where the pest and disease control efficiency does not meet the standard, increase the number of repellent plants configured or introduce repellent plant species with higher plant efficacy index, and re-simulate the effect of the adjusted scheme. Based on the sign and magnitude of the achievement deviation value, the specific steps for directionally modifying the type, quantity, or spatial distribution data of repellent plants in the initial repellent plant configuration scheme are as follows: First, analyze the sign of the achievement deviation value to determine the direction that needs to be enhanced or suppressed. Quantify the adjustment range according to the magnitude of the deviation. For negative deviations, introduce repellent species with higher plant efficacy index or increase the number of plants configured per unit area. For positive deviations, optimize the spatial layout to strengthen the synergistic effect. After modification, the compatibility of allelopathic effects between plants needs to be verified. S303. The updated indicators obtained from the re-simulation are iteratively compared with the preset optimization target. If the overall optimization urgency index drops below the predetermined optimization threshold and all sub-indicators reach the preset range, the current preliminary repellent plant configuration scheme is determined to be an optimized convergence scheme, and the output is the optimized repellent plant configuration scheme. If the convergence standard is not met, the dynamic feedback adjustment process is repeated until the output conditions are met.

[0030] The preset optimization targets are: the optimal value for pest and disease control efficiency is ≥90%, the target for plant community biomass growth is ≥10%, and the threshold for ecological balance index is ≥0.8; the preset optimization threshold is the comprehensive optimization urgency index ≤0.1; the preset range is the allowable deviation of each sub-indicator ±5%, which is based on the statistical analysis of historical best tea garden management data.

[0031] This embodiment achieves precise control of repellent plant configuration through dynamic interactive simulation and iterative optimization mechanism, significantly improving the efficiency of pest and disease control and ecological coordination. At the same time, it enhances the system's adaptability through the integration of weighted indicators, providing a closed-loop optimization path for green management of tea gardens.

[0032] The specific content of step S4 is as follows: Based on the optimized repellent plant configuration scheme, determine the target repellent plant species and their required specific resistance traits and allelochemical secretion capacity. Based on the target repellent plant species and their required specific resistance traits and allelochemical secretion capacity, formulate targeted screening criteria. Then, apply the targeted screening criteria to screen the existing preset germplasm resources in the physical nursery. For individuals that meet the criteria, use artificial pollination or molecular marker-assisted selection technology for targeted breeding. During the breeding process, continuously monitor the specific resistance traits and allelochemical secretion capacity of the offspring repellent plants to cultivate high-quality seedlings that meet the requirements of the optimized repellent plant configuration scheme and have stable traits.

[0033] Step S5 further includes: after the actual planting of the target tea garden is completed, continuously monitor the actual occurrence of pests and diseases and the growth status of the plants in the target tea garden, and feed the data of the actual occurrence of pests and diseases and the growth status of the plants back to the virtual planning space. Compare and analyze the data with the expected effect simulated in the virtual planning space in real time, calculate the deviation coefficient between the actual value and the simulated value, and if the deviation coefficient exceeds the preset dynamic adjustment threshold, automatically trigger the optimization iteration process of step S3, regenerate the optimized repellent plant configuration scheme, and adjust the repellent plant strip planted in the field according to the updated optimized repellent plant configuration scheme. The adjustment methods include local replanting, thinning, or variety replacement to ensure the long-term control effect stability and ecological adaptability of the repellent plant strip.

[0034] Specific example: Taking a virtual sub-plot in the target tea garden as an example, its historical pest and disease data shows an occurrence frequency of 0.8 and a severity of 0.9. Using a preset first weight (occurrence frequency weight 0.3, severity weight 0.7), the pest and disease risk index is calculated as: 0.8 × 0.3 + 0.9 × 0.7 = 0.87. Since 0.87 is higher than the first preset risk threshold of 0.75, this sub-plot is determined to be a high-risk area. For the candidate repellent plant lemongrass, its growth characteristic data shows a soil adaptability of 0.7 and a target pest inhibition rate of 0.9. Using preset rules, the plant efficacy index is calculated as: soil adaptability weight 0.4, target pest inhibition rate weight 0.6, with an initial efficacy value of 0.7 × 0.4 + 0.9 × 0.6 = 0.82. Since the plant efficacy index of 0.82 is higher than the first preset threshold of 0.8, this plant is allocated a first preset basic quantity extraction ratio of 40%. Assuming the theoretical maximum planting capacity of this virtual subplot is 100 plants, the initial configuration of lemongrass is 100 × 40% = 40 plants. Subsequently, through analysis of ecological interaction effects, it was found that lemongrass and marigolds in the adjacent subplot have a synergistic repellency effect. The configuration quantity is increased to 48 plants according to the preset first gain ratio of 20%. Finally, an optimized scheme is generated and passes the matching degree verification (the ratio of comprehensive repellency effectiveness index to risk index reaches 1.3, which is higher than the high matching threshold of 1.2), thus completing the repellency plant configuration of this virtual subplot.

[0035] This example achieves precise matching of repellent plant configurations by quantitatively calculating the risk index and plant efficacy index; based on dynamic optimization and synergistic effect adjustment, it improves the efficiency of pest and disease control to over 90%, while enhancing ecosystem stability and forming a reusable green pest control paradigm for tea gardens.

[0036] The above are merely embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A green pest control method for tea gardens based on repellent plant zones, characterized by: Includes the following steps: S1. Collect on-site environmental data of the target tea garden, and simultaneously collect the self-attribute data of various candidate repellent plants. Integrate the on-site environmental data and the self-attribute data to form a basic dataset for tea garden planning. S2. Based on the tea garden planning basic dataset, construct a virtual planning space, and within the virtual planning space, screen and spatially configure various candidate repellent plants to generate a preliminary repellent plant configuration scheme. S3. In the virtual planning space, simulate the expected effect of the preliminary repellent plant configuration scheme within a preset period; adjust the preliminary repellent plant configuration scheme according to the difference between the simulated expected effect and the preset optimization target until the difference reaches the predetermined standard, and output the optimized repellent plant configuration scheme. S4. Based on the optimized repellent plant configuration scheme, in a physical nursery, the target repellent plants in the optimized repellent plant configuration scheme are targeted for screening or breeding to cultivate high-quality seedlings that meet the requirements of the optimized repellent plant configuration scheme. S5. Implement the optimized repellent plant configuration scheme in the target tea garden, and complete the on-site planting of the repellent plants using the cultivated high-quality seedlings.

2. The green pest control method for tea gardens based on repellent plant strips according to claim 1, characterized in that: The on-site environmental data in step S1 includes the topographic data, soil data, and historical pest and disease data of the target tea garden; The intrinsic attribute data includes the growth characteristics and insect repellent efficacy data of the candidate repellent plants; The actual environmental data and the self-attribute data are collected through a preset data acquisition terminal and data interface.

3. The green pest control method for tea gardens based on repellent plant strips according to claim 2, characterized in that: The method for constructing the virtual planning space in step S2 is as follows: S201. Call the preset twin space model, map the terrain data in the actual environment data to the twin space model, generate a virtual terrain framework consistent with the target tea garden terrain, integrate the soil data in the actual environment data into the virtual terrain framework, establish virtual plots, divide the virtual plots into multiple virtual sub-plots according to the soil data, and attach a unique preset spatial identifier label to each virtual sub-plot. Match the historical pest and disease data in the actual environment data with the spatial range of the virtual sub-plots according to their recorded geographical locations, and bind the occurrence frequency and severity data in the successfully matched historical pest and disease data to the spatial identifier label of the matched virtual sub-plot. S202. Based on the frequency and severity data of occurrence in the completed virtual sub-plots and the historical pest and disease data they are bound to, and according to the growth characteristic data and pest-repelling efficacy data in the self-attribute data of the candidate repellent plants, an extraction ratio is generated. The basic quantity of each candidate repellent plant is selected according to the extraction ratio, and each candidate repellent plant is allocated to the virtual sub-plots according to the basic quantity to construct a virtual planning space.

4. The green pest control method for tea gardens based on repellent plant strips according to claim 3, characterized in that: The step of generating the extraction ratio in step S202 is as follows: S2021. Based on the frequency and severity data of the historical pest and disease data bound to the virtual sub-plot, calculate a unique pest and disease risk index for each virtual sub-plot. The frequency data quantifies the frequency of historical pest and disease events occurring on this virtual sub-plot within a preset historical period, and the severity data represents the intensity level of the impact of historical pest and disease events on the target tea garden. The frequency value and the intensity level value are weighted and fused according to a preset first weight to generate the pest and disease risk index of the virtual sub-plot. S2022. Based on the growth characteristic data and insect repellency data of the candidate repellent plants, the growth characteristic data and insect repellency data are merged according to a preset rule to generate a plant efficacy index for each candidate repellent plant. S2023. For each virtual sub-plot, compare the pest and disease risk index with the plant efficacy index of each candidate repellent plant to be allocated to the virtual sub-plot; if the pest and disease risk index of the virtual sub-plot is higher than a first preset risk threshold, then extract a first preset basic quantity ratio for the candidate repellent plants whose plant efficacy index is higher than the first preset threshold. If the pest and disease risk index of the virtual sub-plot is between the first preset risk threshold and the second preset risk threshold, then the second preset basic quantity extraction ratio is selected; if the pest and disease risk index of the virtual sub-plot is less than the first preset risk threshold, then the third preset basic quantity extraction ratio is allocated according to the plant efficacy index, and the specific extraction ratio of each candidate repellent plant in each virtual sub-plot is determined.

5. The green pest control method for tea gardens based on repellent plant strips according to claim 4, characterized in that: The method for generating the preliminary repellent plant configuration scheme in step S2 is as follows: S203. Based on the extraction ratio of each candidate repellent plant determined for each virtual sub-plot in the virtual planning space, calculate the initial configuration quantity of each candidate repellent plant in different virtual sub-plots, and generate an initial spatial distribution matrix of candidate repellent plants by combining the actual geographical location corresponding to the spatial identifier label of the virtual sub-plot. S204. Based on the initial spatial distribution matrix, analyze the potential ecological interaction effects when different candidate repellent plants are configured in the same or adjacent virtual sub-plots. For candidate repellent plant combinations with significant synergistic or cooperative repellent effects in the potential ecological interaction effects, increase the initial configuration quantity of each candidate repellent plant by a preset first gain ratio based on the initial configuration quantity. For candidate repellent plant combinations with significant antagonistic effects or resource competition exceeding a preset competition threshold in the potential ecological interaction effects, adjust the spatial relative position of each candidate repellent plant in the candidate repellent plant combination, or reduce the initial configuration quantity of each candidate repellent plant by a preset first inhibition ratio to form an optimized plant combination spatial distribution scheme. S205. The optimized plant combination spatial distribution scheme is matched with the pest and disease risk index bound to each virtual sub-plot in the virtual planning space. The specific verification method is as follows: the ratio of the comprehensive repellency efficiency index of the candidate repellent plant combination allocated to each virtual sub-plot to the pest and disease risk index of the virtual sub-plot is calculated. If the ratio is higher than the preset high matching threshold, the optimized plant combination spatial distribution scheme of the virtual sub-plot is determined to be at the preset high protection efficiency level. If the ratio is between the preset high matching threshold and the preset low matching threshold, the optimized plant combination spatial distribution scheme of the virtual sub-plot is determined to be at the preset medium protection efficiency level. If the ratio is lower than the preset low matching threshold, the optimized plant combination spatial distribution scheme of the virtual sub-plot is determined to be at the preset low protection efficiency level, and a preset priority adjustment procedure needs to be initiated. After verifying and adjusting all the virtual sub-plots, the output is a preliminary repellent plant configuration scheme.

6. The green pest control method for tea gardens based on repellent plant strips according to claim 5, characterized in that: The specific steps in step S3, which involve simulating the expected effects of the preliminary repellent plant configuration scheme within a preset period in the virtual planning space, are as follows: In the virtual planning space, load the types, quantities, and spatial distribution data of the repellent plants defined in the preliminary repellent plant configuration scheme, and set the time parameters for the preset period; based on the pest and disease risk index of the virtual sub-plot and the plant efficacy index of the configured repellent plants, simulate the dynamic interaction process between the pest and disease population and the repellent plant community within the preset period. This dynamic interaction process includes the migration and diffusion patterns of pests and diseases, the allelopathic release effect of the repellent plants, and the attraction and colonization behavior of natural enemy insects; and obtain the expected effects, such as the pest and disease control efficiency, plant community biomass, and ecological balance index, at the end of the preset period through simulation calculations.

7. The green pest control method for tea gardens based on repellent plant strips according to claim 6, characterized in that: The method for outputting the optimized repellent plant configuration scheme in step S3 is as follows: S301. Based on the expected effects of pest and disease control efficiency, plant community biomass and ecological balance index at the end of the preset period obtained from the simulation, compare them item by item with the target control efficiency, target biomass and target ecological balance index in the preset optimization targets, calculate the achievement deviation value of each indicator, and merge the achievement deviation values ​​of each item according to the preset second weight to generate a comprehensive optimization urgency index, and determine the optimization priority sequence based on the comprehensive optimization urgency index. S302. For the highest priority indicator in the optimization priority sequence, based on the sign and magnitude of the realization deviation value, modify the type, quantity or spatial distribution data of the repellent plants in the preliminary repellent plant configuration scheme, and re-simulate the expected effect within the preset period based on the modified preliminary repellent plant configuration scheme to obtain the updated pest and disease control efficiency, plant community biomass and ecological balance index. S303. The updated indicators obtained from the re-simulation are iteratively compared with the preset optimization target. If the overall optimization urgency index drops below the predetermined optimization threshold and all sub-indicators reach the preset range, the current preliminary repellent plant configuration scheme is determined to be an optimized convergence scheme, and the output is the optimized repellent plant configuration scheme.

8. The green pest control method for tea gardens based on repellent plant strips according to claim 7, characterized in that: The specific content of step S4 is as follows: Based on the optimized repellent plant configuration scheme, determine the target repellent plant species and their required specific resistance traits and allelochemical secretion capacity, and formulate targeted screening criteria based on the target repellent plant species and their required specific resistance traits and allelochemical secretion capacity. Then, apply the targeted screening criteria to screen the existing preset germplasm resources in a physical nursery. For individuals that meet the criteria, use artificial pollination or molecular marker-assisted selection technology for targeted breeding. During the breeding process, continuously monitor the specific resistance traits and allelochemical secretion capacity of the offspring repellent plants to cultivate high-quality seedlings that meet the requirements of the optimized repellent plant configuration scheme and have stable traits.

9. The green pest control method for tea gardens based on repellent plant strips according to claim 8, characterized in that: Step S5 further includes: after the actual planting of the target tea garden is completed, continuously monitoring the actual occurrence of pests and diseases and the growth status of the plants in the target tea garden, and feeding back the data on the actual occurrence of pests and diseases and the growth status of the plants to the virtual planning space, comparing and analyzing the simulated expected effect in the virtual planning space in real time, calculating the deviation coefficient between the actual value and the simulated value, and if the deviation coefficient exceeds the preset dynamic adjustment threshold, automatically triggering the optimization iteration process of step S3, regenerating the optimized repellent plant configuration scheme, and adjusting the repellent plant strip planted in the actual planting according to the updated optimized repellent plant configuration scheme.

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