Resource scheduling configuration method and system applied to litchi insect pest prevention and control

Abstract

The invention relates to the technical field of litchi insect pest prevention and control, in particular to a resource scheduling configuration method and system applied to litchi insect pest prevention and control. If the prevention and control resource analysis result shows that static attribute prevention and control resources are needed, establishing a dynamic invasion range of the litchi insect pest invasion sub-planting area according to a dynamic invasion approach of the litchi insect pest type, and performing scheduling configuration of the static attribute prevention and control resources based on negotiation and membership operation of the dynamic invasion range and the global static prevention and control sensing area; and if the prevention and control resource analysis result shows that the dynamic attribute prevention and control resources are needed, determining the invasion severity of the litchi pests to the litchi leaves and fruits based on the invasion rate, and scheduling the target dynamic attribute prevention and control resources with the maximum integrating degree according to the invasion severity to perform prevention and control. According to the method, the dynamic attribute prevention and control resources and the static attribute prevention and control resources can be reasonably scheduled and configured according to the attack severity and trend of litchi pests to the litchi garden, so that the use of the prevention and control resources is optimized, and the prevention and control effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of litchi pest control, and in particular to a resource scheduling configuration method and system applied to litchi pest control. Background Art

[0002] Resource scheduling for litchi pest control is mainly aimed at effective resource management and scheduling for the monitoring, prevention and control of different types of litchi pests in the field of litchi agricultural planting. Scientific and reasonable allocation of resources can effectively improve the efficiency of pest control, reduce the negative impact on the environment, and save costs. However, the existing resource scheduling technology for litchi pest control is relatively general. It can usually only perform global scheduling of all control resources in the litchi garden. It is difficult to evenly schedule certain static control resources or dynamic control resources according to the degree of pest invasion. This may cause errors, confusion, shortages or redundant scheduling in the scheduling of litchi pest control resources, greatly reducing the reasonable utilization rate of litchi pest control resources, resulting in deviations in the effect of litchi pest control; and traditional technology is difficult to optimize the global scheduling configuration of resources according to the global invasion severity of litchi pests, thereby delaying the best time for litchi pest control and affecting the quality and efficiency of subsequent litchi planting. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a resource scheduling configuration method and system for litchi pest control.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] A first aspect of the present invention provides a resource scheduling configuration method for litchi pest control, comprising the following steps:

[0006] S102: Obtaining control requirements for litchi pest species, constructing a dynamic activity prediction model for litchi pest species, predicting pest outbreak frequencies based on control requirements in the dynamic activity prediction model, and obtaining sub-activity trend transition prediction distributions for litchi pest species at consecutive future seasonal time series nodes;

[0007] S104: determining the activity transition scale of litchi pest species transition to each future season time series node according to the survival curve layout field created by the sub-activity trend transition prediction distribution, analyzing the prevention and control resources required for the sub-planting area based on the activity transition scale, and generating a prevention and control analysis result;

[0008] S106: If the analysis result of the prevention and control resources shows that static attribute prevention and control resources are needed, a dynamic invasion range of the litchi pest-invaded sub-planting area is established according to the dynamic invasion pathway of the litchi pest species, and a scheduling configuration of the static attribute prevention and control resources is performed based on the negotiation and membership operation between the dynamic invasion range and the global static prevention and control perception area to generate a first scheduling configuration plan;

[0009] S108: If the analysis result of the prevention and control resources shows that dynamic attribute prevention and control resources are needed, the severity of the invasion of litchi pests on litchi leaves and fruits is determined based on the invasion rate, and the target dynamic attribute prevention and control resources with the maximum fit are dispatched for prevention and control according to the invasion severity, and a second scheduling configuration plan is generated;

[0010] S110: Generate a normal-alert control area pattern through hash dislocation calculation analysis of the invasion degree density map and allocate control scheduling priorities according to the optimal control time and space nodes, and plan the optimal scheduling path for control resources based on the control scheduling priorities to improve the scheduling efficiency of litchi pest control resources.

[0011] More specifically, the step S102 includes the following steps:

[0012] Obtaining a planting area map of a litchi garden and monitoring terminal layout points of the planting area map, dividing the planting area map into a plurality of sub-planting areas based on the monitoring terminal layout points, using a monitoring terminal to capture real-time image data of each sub-planting area in real time and analyzing and extracting the contained feature information, so as to determine the types of litchi pests existing in each sub-planting area;

[0013] Retrieving the monitoring logs of the corresponding monitoring terminals in each sub-planting area according to the monitoring terminal layout points, and extracting several frames of historical activity image data of the litchi pest species in each sub-planting area in a predetermined seasonal time sequence through the monitoring logs;

[0014] Introducing an E-GARCH algorithm, constructing an E-GARCH model for each sub-planting area in the E-GARCH algorithm based on the several frames of historical active image data, and presetting an error term for the activity of litchi pest species in each sub-planting area according to each of the E-GARCH models;

[0015] Creating a maximum likelihood function of the activity of litchi pest species in each sub-planting area based on the error term, performing autocorrelation validation training on the residuals of the E-GARCH model of each sub-planting area based on the maximum likelihood function, and finally generating a dynamic activity prediction model of litchi pest species in each sub-planting area;

[0016] Obtain the prevention and control requirements of litchi pest types, preset the future season time series according to the prevention and control requirements, obtain the pest outbreak frequency of litchi pest types in the future season time series through the big data network, and discretize the future season time series into multiple continuous future season time series nodes based on the pest outbreak frequency;

[0017] The dynamic activity prediction model is used to predict the activity transition trend of litchi pest species in each sub-planting area through future seasonal time series, and the activity transition trend prediction results are obtained. Finally, the sub-activity transition trend prediction distribution of litchi pest species at every two consecutive future seasonal time series nodes is separated from the activity transition trend prediction results.

[0018] More specifically, the step S104 includes the following steps:

[0019] Obtaining a planting scale coefficient of each sub-planting area according to a planting area map of a litchi orchard, and presetting a field boundary constraint threshold of each sub-planting area based on the planting scale coefficient;

[0020] Construct a survival curve domain architecture based on future seasonal time series nodes and planting scale coefficients, expand and constrain the domain boundary of the survival curve domain architecture of each sub-planting area based on the domain boundary constraint threshold, and stop expanding the constraint until the current domain boundary value in the process of expanding the constraint meets the corresponding domain boundary constraint threshold;

[0021] According to the distribution of the predicted transition trend of sub-activities, the survival curve layout is fitted and drawn in the survival curve domain framework completed with the expanded constraints, and the survival curve layout domain of each sub-planting area is obtained;

[0022] The survival curve layout field is used to extract the survival probability fluctuation amplitude of the survival curve layout during the transition of the litchi pest species in each sub-planting area at the adjacent future season time series nodes, and the activity transition scale of the litchi pest species at each adjacent future season time series node is determined according to the survival probability fluctuation amplitude;

[0023] Obtain the static and dynamic control resources that can be dispatched in the litchi orchard, and obtain the standard control scales corresponding to the types of litchi pests for each static and dynamic control resource, and quantify the activity transition scales between each adjacent future seasonal time series node to calculate and generate standardized statistics of the activity transition scales;

[0024] A normal distribution table is constructed based on the standard prevention and control scale, and the critical interval of statistics for each static attribute prevention and control resource to reach the activity transition scale is extracted through the normal distribution table, which is defined as the static statistical critical interval; and the critical interval of statistics for each dynamic attribute prevention and control resource to reach the activity transition scale is extracted, which is defined as the dynamic statistical critical interval;

[0025] If the standardized statistic is in the critical interval of the static statistic, it is determined that the sub-planting area requires static attribute prevention and control resources; if the standardized statistic is in the critical interval of the dynamic statistic, it is determined that the sub-planting area requires dynamic attribute prevention and control resources, and a prevention and control analysis result is generated.

[0026] More specifically, the step S106 includes the following steps:

[0027] If the results of the prevention and control resource analysis show that static-attribute prevention and control resources are needed, the dynamic invasion pathways of litchi pest species are separated from the dynamic activity prediction model based on the activity transition scale of litchi pest species at two consecutive future season time series nodes;

[0028] Obtain a schematic diagram of the spatial layout of various static attribute prevention and control resources in the planting area map, and construct a dynamic invasion and diffusion equation for litchi pest invasion in the sub-planting area based on the activity transition scale of litchi pest species at two consecutive future season time series nodes and the dynamic invasion pathway;

[0029] Creating a virtual domain of spatial layout of static attribute prevention and control resources in the planting area according to the spatial layout schematic diagram, solving the dynamic invasion and diffusion equation to generate a number of diffusion wave values ​​of litchi pest species invasion;

[0030] By performing dynamic invasion fitting of a plurality of the diffusion wave values ​​in a virtual field of spatial layout, a dynamic virtual invasion model of the litchi pest species in the sub-planting area is generated, and a dynamic invasion range of the dynamic virtual invasion model is obtained;

[0031] Obtain the preset static control range of each static attribute control resource, set the perception boundary threshold based on the preset static control range and the spatial layout schematic diagram, plan the global perception area of ​​all static attribute control resources for the planting area map in the spatial layout virtual field until the area boundary value meets the perception boundary threshold, and generate a global static control perception area;

[0032] A perception negotiation area threshold is preset, and the global static prevention and control perception area is divided into M sub-perception areas according to the spatial layout diagram. If a dynamic invasion range and a sub-perception area generate a negotiation area and the area value of the negotiation area is greater than the perception negotiation area threshold, the sub-perception area is marked as a suspicious sub-perception area;

[0033] A clustering calculation is performed on the preset static prevention and control range to which the suspicious sub-perception area belongs to obtain a clustering result. According to the clustering result, the static attribute prevention and control resources belonging to the suspicious sub-perception area are determined and dispatched and configured in the sub-planting area for prevention and control, thereby obtaining a first dispatching configuration plan.

[0034] More specifically, the step S108 includes the following steps:

[0035] If the analysis result of prevention and control resources shows that dynamic attribute prevention and control resources are needed, the prevention and control log of the litchi orchard is used to obtain the invasion rate of litchi pest species on litchi leaves and fruits in the sub-planting area when the corresponding activity transition scale occurs between each future seasonal time series node;

[0036] A vector autoregression model is constructed based on the corresponding activity transition scale of litchi pest species in the sub-planting area between each future seasonal time series node, and the lag term and coefficient matrix of the vector autoregression model are obtained;

[0037] A unit shock is set according to the invasion rate corresponding to each activity transition scale to obtain multiple unit shocks, and the multiple unit shocks are applied one by one to the vector autoregression model to recursively iterate the lag term and coefficient matrix on each future seasonal time series node, and a series of impulse response functions are obtained through recursive iteration;

[0038] Obtain the control strategies for each dynamic attribute control resource in the litchi orchard and the established severity of the attack that each control strategy can control. Determine the severity of the attack of the litchi pest species on the leaves and fruits of the litchi in the sub-planting area at each future seasonal time series node based on a series of impulse response functions.

[0039] By density estimation of each invasion severity, a density map of the invasion degree of litchi pest types in the sub-planting area is constructed, and a density color index between two consecutive future season time series nodes is extracted according to the invasion degree density map, and a predetermined density color index interval that can be controlled by each prevention and control strategy is set based on the predetermined invasion severity;

[0040] Only one or more dynamic attribute control resources that can be executed corresponding to the control strategy whose density color index is within the predetermined density color index range are extracted and marked as the target dynamic attribute control resource. At this time, the actual topographic and geomorphic features of the sub-planting area and the preset topographic and geomorphic feature set that one or more target dynamic attribute control resources are allowed to run are obtained;

[0041] The degree of fit between the actual topographic and geomorphic features and each preset topographic and geomorphic feature in the preset topographic and geomorphic feature set is calculated one by one, and finally the target dynamic attribute control resource corresponding to the preset topographic and geomorphic feature with the maximum fit is extracted and configured for litchi pest control at the future seasonal time series nodes to obtain the second scheduling configuration plan.

[0042] More specifically, the step S110 includes the following steps:

[0043] Preset the warning invasion severity and warning hash misalignment function interval, globally evaluate the invasion severity of the sub-planting area based on the density color index of the invasion degree density map, obtain the global invasion severity, and calculate the hash misalignment function between the global invasion severity and the warning invasion severity of the sub-planting area;

[0044] If the hash dislocation function is not within the warning hash dislocation function interval, the sub-planting area is marked as a normal control area; if the hash dislocation function is within the warning hash dislocation function interval, the sub-planting area is marked as a warning control area, generating a normal-warning control area pattern;

[0045] Obtain the climate season information of the current litchi garden, and retrieve the best prevention and control time and space node of the first scheduling configuration scheme or the second scheduling configuration scheme for the global invasion severity of the current litchi garden in the big data network based on the climate season information and the actual topographic features;

[0046] The necessary prevention and control time is estimated according to the global invasion severity, and the prevention and control response ratio of static attribute prevention and control resources and dynamic attribute prevention and control resources for the ordinary prevention and control area or the alert prevention and control area is calculated based on the necessary prevention and control time and the optimal prevention and control time and space nodes;

[0047] Only the normal control area or alert control area corresponding to the highest control response ratio is extracted and actual control is performed to obtain the actual control duration. The necessary control duration is continuously updated according to the actual control duration to generate a new highest control response ratio. The control scheduling priority of the normal-alert control area pattern is allocated according to the new highest control response ratio.

[0048] Get the current normal-alert control area pattern to display all normal control areas or alert control areas assigned to the required control scheduling priority, all marked as waiting control scheduling areas, and obtain the current scheduling base station map of various static control resources and dynamic control resources and the scheduling path map of the litchi garden;

[0049] Taking the optimal control time-space node as the accurate scheduling node, the Dijkstra algorithm is introduced. Based on the pattern of the scheduling area to be controlled and the current scheduling base station map, the Dijkstra algorithm dynamically plans the optimal scheduling path on the scheduling path map when various static control resources and dynamic control resources reach the accurate scheduling node, so as to improve the scheduling efficiency of litchi pest control resources.

[0050] A second aspect of the present invention provides a resource scheduling and configuration system applied to litchi pest control, the resource scheduling and configuration system applied to litchi pest control comprising a memory and a processor, the memory storing a resource scheduling and configuration method program applied to litchi pest control, when the resource scheduling and configuration method program applied to litchi pest control is executed by the processor, any one of the steps of the resource scheduling and configuration method applied to litchi pest control is implemented.

[0051] The present invention solves the technical defects existing in the background technology, and the beneficial technical effects of the present invention are:

[0052] A dynamic activity prediction model for litchi pest species is constructed, and the frequency of pest outbreaks based on prevention and control requirements is predicted in the dynamic activity prediction model to obtain a predicted distribution of sub-activity trend transitions of litchi pest species at consecutive future seasonal time series nodes; the activity transition scale of litchi pest species that transition to each future seasonal time series node is determined based on the predicted distribution of sub-activity trend transitions, and the prevention and control resources required for sub-planting areas are analyzed based on the activity transition scale to generate a prevention and control resource analysis result; if the prevention and control resource analysis result shows that static attribute prevention and control resources are needed, a dynamic invasion range of litchi pests invading sub-planting areas is established based on the dynamic invasion pathway of the litchi pest species, and static attribute prevention and control resources are scheduled and configured based on the negotiation and membership operation between the dynamic invasion range and the global static prevention and control perception area to generate a first scheduling configuration plan; if the prevention and control resource analysis result shows that dynamic attribute prevention and control resources are needed, the severity of the invasion of litchi pests on litchi leaves and fruits is determined based on the invasion rate, and the target dynamic attribute prevention and control resources with the maximum fit are scheduled for prevention and control according to the invasion severity to generate a second scheduling configuration plan. The present invention can reasonably dispatch and allocate dynamic attribute prevention and control resources and static attribute prevention and control resources in a litchi orchard according to the severity and trend of the invasion of litchi pests on the litchi orchard, optimize the use of prevention and control resources to improve the prevention and control effect, reduce or even eliminate the large-scale invasion and reproduction of litchi pests, and ensure the high-quality growth and planting of litchi. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in 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 described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0054] Figure 1 A first method flow chart of a resource scheduling configuration method applied to litchi pest control is shown;

[0055] Figure 2 A second method flow chart of a resource scheduling configuration method applied to litchi pest control is shown;

[0056] Figure 3 A third method flow chart of a resource scheduling configuration method applied to litchi pest control is shown;

[0057] Figure 4 The system framework diagram of a resource scheduling configuration system applied to litchi pest control is shown. DETAILED DESCRIPTION

[0058] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0060] The first aspect of the present invention provides a resource scheduling configuration method for litchi pest control, such as Figure 1 As shown, the following steps are included:

[0061] S102: Obtaining control requirements for litchi pest species, constructing a dynamic activity prediction model for litchi pest species, predicting pest outbreak frequencies based on control requirements in the dynamic activity prediction model, and obtaining a sub-activity transition trend prediction distribution for litchi pest species at consecutive future seasonal time series nodes;

[0062] S104: determining the activity transition scale of litchi pest species transition to each future season time series node according to the survival curve layout field created by the sub-activity transition trend prediction distribution, analyzing the prevention and control resources required for the sub-planting area based on the activity transition scale, and generating a prevention and control analysis result;

[0063] S106: If the analysis result of the prevention and control resources shows that static attribute prevention and control resources are needed, a dynamic invasion range of the litchi pest-invaded sub-planting area is established according to the dynamic invasion pathway of the litchi pest species, and a scheduling configuration of the static attribute prevention and control resources is performed based on the negotiation and membership operation between the dynamic invasion range and the global static prevention and control perception area to generate a first scheduling configuration plan;

[0064] S108: If the analysis result of the prevention and control resources shows that dynamic attribute prevention and control resources are needed, the severity of the invasion of litchi pests on litchi leaves and fruits is determined based on the invasion rate, and the target dynamic attribute prevention and control resources with the maximum fit are dispatched for prevention and control according to the invasion severity, and a second scheduling configuration plan is generated;

[0065] S110: Generate a normal-alert control area pattern through hash dislocation calculation analysis of the invasion degree density map and allocate control scheduling priorities according to the optimal control time and space nodes, and plan the optimal scheduling path for control resources based on the control scheduling priorities to improve the scheduling efficiency of litchi pest control resources.

[0066] More specifically, the step S102 is as follows: Figure 2 As shown, the specific steps include:

[0067] S202: Obtain a planting area map of the litchi orchard and monitoring terminal layout points of the planting area map, divide the planting area map into a plurality of sub-planting areas based on the monitoring terminal layout points, use the monitoring terminal to take real-time image data of each sub-planting area in real time and analyze and extract the contained feature information to determine the types of litchi pests existing in each sub-planting area;

[0068] S204: Retrieving the monitoring logs of the corresponding monitoring terminals in each sub-planting area according to the monitoring terminal layout points, and extracting several frames of historical activity image data of the litchi pest species in each sub-planting area in a predetermined seasonal time sequence through the monitoring logs;

[0069] S206: Introducing an E-GARCH algorithm, constructing an E-GARCH model for each sub-planting area in the E-GARCH algorithm based on the plurality of frames of historical active image data, and presetting an error term for the activity of litchi pest species in each sub-planting area according to each of the E-GARCH models;

[0070] S208: creating a maximum likelihood function of the activity of litchi pest species in each sub-planting area according to the error term, performing autocorrelation verification training on the residual of the E-GARCH model of each sub-planting area based on the maximum likelihood function, and finally generating a dynamic activity prediction model of litchi pest species in each sub-planting area;

[0071] S210: Obtaining the control requirements of litchi pest types, presetting the future season time series according to the control requirements, obtaining the pest outbreak frequency of the litchi pest types in the future season time series through the big data network, and discretizing the future season time series into multiple continuous future season time series nodes based on the pest outbreak frequency;

[0072] S212: The activity transition trend of the litchi pest species in each sub-planting area through the future season time series is predicted by a dynamic activity prediction model to obtain the activity transition trend prediction result, and finally the sub-activity transition trend prediction distribution of the litchi pest species at every two consecutive future season time series nodes is separated from the activity transition trend prediction result.

[0073] It should be noted that during the lychee planting process, different types of lychee pests usually live on the leaves and fruits of the lychee trees, such as the lychee stink bug, the lychee fruit fly or the lychee stem borer. Since these types of lychee pests have different peak breeding periods, their activity scales and trends in different seasons are uneven. In order to more reasonably schedule and allocate prevention and control resources, it is necessary to analyze the transition distribution of the activity trend of lychee pests; however, the existing prevention and control resource scheduling methods are difficult to accurately and efficiently achieve this effect for the activity trend transition distribution of different types of lychee pests, resulting in unreasonable phenomena such as scheduling confusion, scheduling errors and redundant scheduling of prevention and control resources, which greatly reduces resource utilization. Therefore, this method first determines the types of litchi pests existing in the litchi garden planting area, and calculates the E-GARCH algorithm based on several frames of historical activity image data of the litchi pest types in the established seasonal time series to create a dynamic activity prediction model. The dynamic activity prediction model can accurately and dynamically predict the activity scale, trend and transition distribution of the litchi pest types in different sub-planting areas in the litchi garden, so that the activity status of different litchi pest types in the sub-planting areas can be accurately predicted in advance, which can make the subsequent scheduling and configuration of prevention and control resources more targeted and reasonable, and improve the resource scheduling abnormality. And the prediction of the dynamic activity prediction model in this method is based on the activity characteristics of the litchi pests during seasonal changes, that is, the sub-activity trend transition prediction distribution of the litchi pest types at every two consecutive future seasonal time series nodes, so as to achieve the effect of scheduling prevention and control resources highly around the activity behavior generated during the seasonal reproduction peak of different litchi pests, and ensure that the resource scheduling configuration is more reliable for the prevention and control quality and efficiency of seasonal activities of litchi pests.

[0074] It should be noted that the Chinese name of the E-GARCH algorithm is the exponential generalized autoregressive conditional heteroskedasticity algorithm, which can be used to predict time series data with asymmetric volatility, especially the activity behavior fluctuations of different litchi pests caused by different seasonal changes. The dynamic activity prediction model constructed by the E-GARCH algorithm can effectively describe the volatility clustering phenomenon, that is, larger seasonal changes are often accompanied by larger fluctuations in the activity trend of litchi pests, and smaller seasonal changes are accompanied by smaller fluctuations in the activity trend of litchi pests. This makes it possible to follow and predict the activity trend fluctuations of litchi pests with seasonal changes, thereby improving the robustness of the prediction and ensuring the accuracy of the activity trend transition prediction results.

[0075] More specifically, the step S104 includes the following steps:

[0076] Obtaining a planting scale coefficient of each sub-planting area according to a planting area map of a litchi orchard, and presetting a field boundary constraint threshold of each sub-planting area based on the planting scale coefficient;

[0077] Construct a survival curve domain architecture based on future seasonal time series nodes and planting scale coefficients, expand and constrain the domain boundary of the survival curve domain architecture of each sub-planting area based on the domain boundary constraint threshold, and stop expanding the constraint until the current domain boundary value in the process of expanding the constraint meets the corresponding domain boundary constraint threshold;

[0078] According to the distribution of the predicted transition trend of sub-activities, the survival curve layout is fitted and drawn in the survival curve domain framework completed with the expanded constraints, and the survival curve layout domain of each sub-planting area is obtained;

[0079] The survival curve layout field is used to extract the survival probability fluctuation amplitude of the survival curve layout during the transition of the litchi pest species in each sub-planting area at the adjacent future season time series nodes, and the activity transition scale of the litchi pest species at each adjacent future season time series node is determined according to the survival probability fluctuation amplitude;

[0080] Obtain the static and dynamic control resources that can be dispatched in the litchi orchard, and obtain the standard control scales corresponding to the types of litchi pests for each static and dynamic control resource, and quantify the activity transition scales between each adjacent future seasonal time series node to calculate and generate standardized statistics of the activity transition scales;

[0081] A normal distribution table is constructed based on the standard prevention and control scale, and the critical interval of statistics for each static attribute prevention and control resource to reach the activity transition scale is extracted through the normal distribution table, which is defined as the static statistical critical interval; and the critical interval of statistics for each dynamic attribute prevention and control resource to reach the activity transition scale is extracted, which is defined as the dynamic statistical critical interval;

[0082] If the standardized statistic is in the critical interval of the static statistic, it is determined that the sub-planting area requires static attribute prevention and control resources; if the standardized statistic is in the critical interval of the dynamic statistic, it is determined that the sub-planting area requires dynamic attribute prevention and control resources, and a prevention and control analysis result is generated.

[0083] It should be noted that after predicting the sub-activity trend transition prediction distribution of litchi pest types in the sub-planting area at every two consecutive future seasonal time series nodes, since the sub-activity trend transition prediction distribution describes and expresses the seasonal activity transition status of litchi pests in the sub-planting area, it indirectly reflects the activity transition scale of litchi pests; therefore, this method constructs a survival curve domain architecture for each sub-planting area. The survival curve domain is a multidimensional domain that can fit data based on seasonal time to events to show how the survival rate changes with seasonal time. Therefore, the predicted distribution of sub-activity trend transitions is fitted in the survival curve domain architecture, and the survival curve layout of the activity transition of litchi pests at adjacent future season time series nodes in the survival curve layout domain has a survival probability fluctuation range, which represents the scale of activity transition of litchi pests at adjacent future season time series nodes. Therefore, the activity transition scale of litchi pest types at each future season time series node can be further determined according to the survival probability fluctuation amplitude, thereby providing a reliable analysis basis for the scheduling and configuration of subsequent prevention and control resources, greatly improving the efficient prevention and control performance of prevention and control resource scheduling for the inhibition of the scale of litchi pest activity transitions, and ensuring that the activity status of litchi pests at the nodes of continuous seasonal changes can be scheduled and controlled with highly targeted resources, thereby improving the accuracy of prevention and control scheduling. Finally, the standardized statistics obtained by quantifying the scale of activity transitions through the Mann-Kendall trend test algorithm can be used to analyze the prevention and control resource attributes required for the sub-planting area. This method can calculate the activity transition scale of adjacent future seasonal time series nodes based on the sub-activity transition trend prediction distribution description predicted by litchi pest prediction, and determine the required prevention and control resource attributes according to the activity transition scale, so that litchi pest control in litchi orchards is more accurate, and realizes the reasonable planning and allocation of static attribute prevention and control resources and dynamic attribute prevention and control resources for litchi pests, improves the utilization rate of prevention and control resources, and reduces the scheduling error of prevention and control resources.

[0084] It should be noted that the Chinese name of the Mann-Kendall trend test algorithm is the Mann-Kendall trend test, which can detect whether there is a monotonic upward or downward trend in the time series-based litchi pest activity transition scale data without assuming that the data conforms to a specific distribution. It can make the quantification of the litchi pest activity transition scale more accurate, and through the step of constructing a normal distribution table, the subsequent determination of the attributes of the required prevention and control resources can be more accurate, reduce determination errors, and have high reliability.

[0085] More specifically, the step S106 includes the following steps:

[0086] If the results of the prevention and control resource analysis show that static-attribute prevention and control resources are needed, the dynamic invasion pathways of litchi pest species are separated from the dynamic activity prediction model based on the activity transition scale of litchi pest species at two consecutive future season time series nodes;

[0087] Obtain a schematic diagram of the spatial layout of various static attribute prevention and control resources in the planting area map, and construct a dynamic invasion and diffusion equation for litchi pest invasion in the sub-planting area based on the activity transition scale of litchi pest species at two consecutive future season time series nodes and the dynamic invasion pathway;

[0088] Creating a virtual domain of spatial layout of static attribute prevention and control resources in the planting area according to the spatial layout schematic diagram, solving the dynamic invasion and diffusion equation to generate a number of diffusion wave values ​​of litchi pest species invasion;

[0089] By performing dynamic invasion fitting of a plurality of the diffusion wave values ​​in a virtual field of spatial layout, a dynamic virtual invasion model of the litchi pest species in the sub-planting area is generated, and a dynamic invasion range of the dynamic virtual invasion model is obtained;

[0090] Obtain the preset static control range of each static attribute control resource, set the perception boundary threshold based on the preset static control range and the spatial layout schematic diagram, plan the global perception area of ​​all static attribute control resources for the planting area map in the spatial layout virtual field until the area boundary value meets the perception boundary threshold, and generate a global static control perception area;

[0091] A perception negotiation area threshold is preset, and the global static prevention and control perception area is divided into M sub-perception areas according to the spatial layout diagram. If a dynamic invasion range and a sub-perception area generate a negotiation area and the area value of the negotiation area is greater than the perception negotiation area threshold, the sub-perception area is marked as a suspicious sub-perception area;

[0092] A clustering calculation is performed on the preset static prevention and control range to which the suspicious sub-perception area belongs to obtain a clustering result. According to the clustering result, the static attribute prevention and control resources belonging to the suspicious sub-perception area are determined and dispatched and configured in the sub-planting area for prevention and control, thereby obtaining a first dispatching configuration plan.

[0093] It should be noted that when the sub-planting area needs static attribute control resources, since the control position and control posture of static attribute control resources in the litchi orchard are relatively fixed, for example, the pest trapping equipment set up in the litchi orchard emits luring light or smell to attract litchi pests for capture, thereby achieving the trapping effect. Therefore, the static attribute control resources for litchi pest control are mainly based on their invasion characteristics to perform control, that is, when the invasion behavior of litchi pests is within the control range of static attribute control resources, it is necessary to dispatch them to start the control and killing of litchi pests. However, different types of static attribute control resources are set up in the litchi orchard, and the distribution position of each static attribute control resource is relatively fixed. If the activity transition scale of litchi pests is directly determined by whether it is within the control range of static attribute control resources, it may cause large errors in the invasion trajectory analysis, resulting in scheduling configuration errors and affecting the accurate scheduling of static attribute control resources. Therefore, this method separates the dynamic invasion pathways of litchi pest species from the dynamic activity prediction model, and constructs a dynamic invasion diffusion equation for litchi pest invasion in the sub-planting area in combination with the activity transition scale. The dynamic invasion diffusion equation can accurately describe the dynamic invasion diffusion state of the litchi pest population, and the diffusion wave value solved by the dynamic invasion diffusion equation expresses that the density of the litchi pest population will form an extended form over time, providing a reliable data basis for the invasion diffusion state and density form for the subsequent dynamic virtual invasion model fitting, making the dynamic virtual invasion model more realistic and accurate in visualizing the invasion of litchi pests, and ensuring that the dynamic invasion range obtained is more standardized.

[0094] It should be noted that static prevention and control resources include pest trapping equipment, regional timed spraying equipment and regional monitoring equipment. Static attribute control resources are usually set with a preset static control range, and the method plans a global static control perception area in the litchi planting area through the preset static control range, and the global static control perception area covers the preset static control range of the desired static attribute control resources in the litchi planting area, and then performs clustering membership calculation on the suspicious sub-perception area in the global static control perception area where the dynamic invasion range is located; wherein, if the dynamic invasion range and the sub-perception area generate a negotiation area and the area value of the negotiation area is greater than the perception negotiation area threshold, it means that the invasion activities of litchi pests in the sub-perception area at this time are relatively extensive and frequent, so it is necessary to activate the static attribute control resources to which the sub-perception area belongs to start to capture or kill the litchi pests that are invading, so that the static attribute control resources that need to be scheduled and started during the litchi pest invasion process can be quickly and accurately determined, which reduces a large number of calculation steps compared to traditional scheduling technology, realizes the scientific and reasonable scheduling configuration of static attribute control resources, and improves the efficient killing rate and control quality of static attribute control resources for litchi pests.

[0095] More specifically, the step S108 is as follows: Figure 3 As shown, the specific steps include:

[0096] S302: If the analysis result of the prevention and control resources shows that dynamic attribute prevention and control resources are needed, the attack rate of the litchi pest species in the sub-planting area on the litchi leaves and fruits when the corresponding activity transition scale is generated between each future seasonal time series node is obtained through the prevention and control log of the litchi orchard;

[0097] S304: constructing a vector autoregression model based on the corresponding activity transition scale of litchi pest species in the sub-planting area between each future seasonal time series node, and obtaining the lag term and coefficient matrix of the vector autoregression model;

[0098] S306: setting a unit shock according to the invasion rate corresponding to each activity transition scale to obtain multiple unit shocks, applying the multiple unit shocks to the vector autoregression model one by one to recursively iterate the lag term and coefficient matrix on each future seasonal time series node, and obtaining a series of impulse response functions through recursive iteration;

[0099] S308: Obtain the control strategy for each dynamic attribute control resource in the litchi orchard for the litchi pest type and the established invasion severity that can be controlled by each control strategy, and determine the invasion severity of the litchi pest type in the sub-planting area on the litchi leaves and fruits at each future seasonal time series node according to a series of impulse response functions;

[0100] S310: constructing an invasion degree density map of litchi pest types in the sub-planting area by density estimation of each invasion severity, extracting a density color index between two consecutive future season time series nodes according to the invasion degree density map, and setting a predetermined density color index interval that can be controlled by each prevention and control strategy based on a predetermined invasion severity;

[0101] S312: Only one or more dynamic attribute control resources that can be executed corresponding to the control strategy whose density color index is within the predetermined density color index range are extracted and marked as target dynamic attribute control resources. At this time, the actual topographic and geomorphic features of the sub-planting area and the preset topographic and geomorphic feature set that one or more target dynamic attribute control resources are allowed to run are obtained;

[0102] S314: Calculate the degree of fit between the actual topographic and geomorphic features and each preset topographic and geomorphic feature in the preset topographic and geomorphic feature set one by one, and finally extract the target dynamic attribute control resource scheduling corresponding to the preset topographic and geomorphic feature with the maximum fit, and configure it at the future seasonal time series node to carry out litchi pest control, and obtain the second scheduling configuration plan.

[0103] It should be noted that when the sub-planting area needs dynamic attribute prevention and control resources, due to the wide variety of dynamic attribute prevention and control resources, the prevention and control functions and effects achieved by different dynamic attribute prevention and control resources are different, and dynamic attribute prevention and control resources are usually mobile, such as controlling the aerial pesticide spraying effect of spraying drones to kill and control litchi pests. Due to the movable characteristics of dynamic attribute prevention and control resources, they can be arbitrarily moved and dispatched to the litchi pest infestation area that needs to be controlled. At the same time, the activity transition scale of litchi pests changes with the seasonal time series. Therefore, the dynamic attribute prevention and control resources for litchi pest control need to be targeted for prevention and control according to the severity of the invasion at the seasonal time series nodes, which has higher scheduling and allocation requirements. This method constructs a vector autoregression model through the invasion rate of litchi pests when the corresponding activity transition scale is generated between each future seasonal time series node. When a certain activity transition scale variable in the vector autoregression model is impacted by the invasion rate, other activity transition scale variables will make corresponding dynamic responses, thereby generating a series of impulse response functions reflecting the corresponding invasion severity of the activity transition scale under the influence of the invasion rate, so as to further determine the invasion severity of the litchi pest species on the litchi leaves and fruits at each future seasonal time series node. The invasion severity is used as the prevention and control basis for seasonal time series changes in each sub-planting area, which can make the scheduling and configuration of dynamic attribute prevention and control resources more reasonable, accurate and reliable.

[0104] It should be noted that dynamic attribute prevention and control resources include spraying vehicles, monitoring drones, pesticide application drones, remote sensing monitoring aircraft, and spraying personnel. From the above, it can be seen that the severity of litchi pests may be different during the temporal changes of adjacent future seasons, so it can be visualized by constructing an invasion degree density map. Moreover, each dynamic attribute prevention and control resource has multiple prevention and control strategies, and each prevention and control strategy corresponds to the prevention and control of different invasion severity. Therefore, this method determines whether the dynamic attribute prevention and control resource is suitable for the prevention and control of the invasion severity at the future seasonal time series node by whether the density color index displayed on the invasion degree density map is in the established density color index interval that can be controlled by each prevention and control strategy, so as to realize the preliminary screening of the dynamic attribute prevention and control resources required at each future seasonal time series node; finally, the degree of fit is calculated based on the preset topographic and geomorphic features allowed to operate by the target dynamic attribute prevention and control resource and the actual topographic and geomorphic features of the sub-planting area, so as to realize secondary screening of the dynamic attribute prevention and control resources, thereby improving the scheduling and configuration refinement of the dynamic attribute prevention and control resources, and significantly improving the scheduling and control efficiency and quality of the dynamic attribute prevention and control resources for litchi pests, reducing the waste of resources and the negative impact on the environment caused by unreasonable resource scheduling, saving scheduling cost output, and achieving high economic benefits.

[0105] More specifically, the step S110 includes the following steps:

[0106] Preset the warning invasion severity and warning hash misalignment function interval, globally evaluate the invasion severity of the sub-planting area based on the density color index of the invasion degree density map, obtain the global invasion severity, and calculate the hash misalignment function between the global invasion severity and the warning invasion severity of the sub-planting area;

[0107] If the hash dislocation function is not within the warning hash dislocation function interval, the sub-planting area is marked as a normal control area; if the hash dislocation function is within the warning hash dislocation function interval, the sub-planting area is marked as a warning control area, generating a normal-warning control area pattern;

[0108] Obtain the climate season information of the current litchi garden, and retrieve the best prevention and control time and space node of the first scheduling configuration scheme or the second scheduling configuration scheme for the global invasion severity of the current litchi garden in the big data network based on the climate season information and the actual topographic features;

[0109] The necessary prevention and control time is estimated according to the global invasion severity, and the prevention and control response ratio of static attribute prevention and control resources and dynamic attribute prevention and control resources for the ordinary prevention and control area or the alert prevention and control area is calculated based on the necessary prevention and control time and the optimal prevention and control time and space nodes;

[0110] Only the normal control area or alert control area corresponding to the highest control response ratio is extracted and actual control is performed to obtain the actual control duration. The necessary control duration is continuously updated according to the actual control duration to generate a new highest control response ratio. The control scheduling priority of the normal-alert control area pattern is allocated according to the new highest control response ratio.

[0111] Obtain the current normal-alert control area pattern to display all normal control areas or alert control areas assigned to the required control scheduling priority, all of which are marked as areas to be controlled and scheduled, and at the same time obtain the current scheduling base station map of various static attribute control resources and dynamic attribute control resources and the scheduling path map of the litchi garden;

[0112] Taking the optimal control time-space node as the accurate scheduling node, the Dijkstra algorithm is introduced. Based on the pattern of the scheduling area to be controlled and the current scheduling base station map, the Dijkstra algorithm dynamically plans the optimal scheduling path on the scheduling path map when various static attribute control resources and dynamic attribute control resources reach the accurate scheduling node, so as to improve the scheduling efficiency of litchi pest control resources.

[0113] It should be noted that some litchi pests may have the ability to fly or be more agile in movement, which may cause the distribution of litchi pests in some areas to be more dense, that is, the overall invasion severity of the area is higher, while the distribution of litchi pests in some areas is relatively sparse, that is, the overall invasion severity is lower, resulting in irregular distribution of pests in litchi planting areas. Therefore, the scheduling of static attribute control resources and dynamic attribute control resources needs to fully consider the changes in the priority pattern of the global invasion severity of litchi pests for configuration. Generally speaking, priority is given to concentrating prevention and control forces on areas where litchi pests are most serious. Therefore, this method first evaluates the global invasion severity of each sub-planting area based on the density color index of the invasion density map, and the hash dislocation function reflects the deviation of the global invasion severity from the required degree of warning invasion severity. If the hash dislocation function is not within the warning hash dislocation function interval, it means that the severity of litchi pest invasion in the sub-planting area is low and has not reached the level of warning severity, so it is marked as a normal prevention and control area; otherwise, it means that the severity of litchi pest invasion in the sub-planting area is relatively high and has reached the level of warning severity, so it is marked as a warning prevention and control area. Among them, the best control time and space node is a node of the best control time and control space for litchi pest control under the current climate season and actual terrain characteristics; then, through the analysis and calculation of the highest control response ratio, the scheduling priority of various control resources can be rationally allocated for the normal-alert control area pattern, balancing the fairness of resource scheduling for different severity levels of litchi pests, avoiding resource competition, repeated scheduling and resource shortage in the scheduling of control resources in different planting areas, optimizing resource scheduling balance, and improving the utilization rate of control resources. The Chinese name of the Dijkstra algorithm is the Dijkstra algorithm. Finally, the Dijkstra algorithm can quickly, efficiently and accurately find the shortest path of various control resources from the scheduling base station node to the accurate scheduling node, thereby improving the scheduling efficiency of litchi pest control resources.

[0114] In addition, the resource scheduling configuration method applied to litchi pest control also includes the following steps:

[0115] Obtaining the control demand for litchi pests and the control scale of litchi pests in the target planting area, dispatching and configuring control resources and equipment to control litchi pests in the target planting area according to the control scale, and obtaining the actual reproduction inhibition rate of litchi pests in the target planting area;

[0116] The ideal reproduction inhibition rate is obtained through the prevention and control requirements. If the actual reproduction inhibition rate does not reach the ideal reproduction inhibition rate, the actual pest control data of the prevention and control resource equipment is obtained at this time;

[0117] Obtaining a product model of the prevention and control resource equipment, and based on the product model, retrieving in the big data network the standard pest control data output when the prevention and control resource equipment reaches an ideal reproduction inhibition rate;

[0118] The Gower distance method is introduced to calculate the Gower distance between the actual pest control data and the standard pest control data, and the control drift amplitude of the actual pest control data compared with the standard pest control data is determined according to the Gower distance;

[0119] Obtain the adjustable range of the control working condition of the current control resource equipment, and determine the control working condition control parameters necessary for the current control resource equipment to achieve the ideal reproduction rate according to the control drift amplitude. If the control working condition control parameters exceed the adjustable range, obtain the target product model series of the control resource equipment adapted to the control working condition control parameters based on big data;

[0120] Obtain the established product model of each prevention and control resource equipment in the target planting area, introduce the K-means clustering algorithm to cluster the established product model of each prevention and control resource equipment into the target product model series, generate clustering results, obtain the list of prevention and control resource equipment that meets the target product model series in the target planting area through the clustering results, and replace the current prevention and control resource equipment according to the list of prevention and control resource equipment.

[0121] It should be noted that due to the product characteristics of some control resources, the actual pest control quality and efficiency may be difficult to achieve the ideal effect when controlling litchi pests, which makes it difficult for litchi pests to be ideally suppressed, which in turn promotes the reproduction and growth of litchi pests, causing the proliferation of litchi pests and reducing the quality of litchi planting. Therefore, this method can analyze the drift amplitude of the actual pest control data compared with the standard pest control data based on the ideal reproduction inhibition rate, and select and determine the list of control resource equipment that meets the target product model series according to the drift amplitude to replace the current control resource equipment, so that the control resources dispatched during litchi pest control can play a control condition performance that achieves the ideal reproduction inhibition effect, ensuring that litchi pests can be killed and eliminated to the maximum extent, thereby reducing the degree of harm of litchi pests to litchi planting, and then improving the quality of litchi planting benefits. Among them, the Chinese name of the Gower distance method is the high-dimensional distance method.

[0122] In addition, the resource scheduling configuration method applied to litchi pest control also includes the following steps:

[0123] Obtain the IoT log of the target litchi planting area and preset N consecutive collection timestamps, extract the invasion scale parameters of litchi pests collected at N consecutive collection timestamps through the IoT log, and obtain N consecutive invasion scale parameters;

[0124] Calculating the invasion scale change of the litchi pest species in combination with the N continuous invasion scale parameters to obtain the invasion scale change rate of the litchi pest species;

[0125] If the invasion scale change rate is greater than the preset invasion scale change rate, multiple frames of continuous image data of the litchi pest species in the target litchi planting area at each acquisition time stamp are obtained through a camera, and an edge detection algorithm is introduced to calculate the multiple frames of the continuous image data to generate the temporal growth scale characteristics of the litchi pest species;

[0126] Based on the temporal growth scale characteristics of the litchi pest species, the actual reproduction rate of the litchi pest species is retrieved in the big data network, and the starting growth time node of the litchi pest species is obtained by calculating the reproduction rate and the invasion scale change rate of the litchi pest species;

[0127] Acquire the prevention and control resource equipment that performs the prevention and control scheduling task at the starting growth time node, and at the same time acquire the preset prevention and control task amount and current prevention and control rate of the prevention and control resource equipment for the target litchi planting area;

[0128] Calculate the actual control time of the control resource equipment for the target litchi planting area according to the preset control task volume and the current control rate, and extract the established control time based on the control demand;

[0129] If the actual prevention and control time exceeds the established prevention and control time, the deviation of the actual prevention and control time compared with the established prevention and control time is calculated. According to the prevention and control time deviation, the number of prevention and control resources and equipment is increased, and the prevention and control rate of the current prevention and control resources and equipment is optimized to improve the prevention and control efficiency.

[0130] It should be noted that, in the process of controlling litchi pests during the high-score period of reproduction, some control resources often cause the control of litchi pests to be overtime due to their own abnormal conditions or external factors, which greatly reduces the control efficiency and is not conducive to the efficient pest maintenance effect of litchi planting. Therefore, this method can analyze the invasion scale change rate of litchi pests to deduce the starting growth time node of litchi pest species, thereby determining the optimal control time node of litchi pests, and further calculating the task completion time of the control resources on the node, wherein, if the actual control time exceeds the established control time, it means that the efficiency of the control resource to complete the control task is slow, and it is difficult to complete it efficiently within the specified time, so it is necessary to add a certain number of control resource equipment and optimize the control rate of the current control resource equipment, and the scheduling optimization effect of the control resource can be achieved through this method, so as to improve the control efficiency of litchi pests and avoid the low control efficiency of the control resources and affect the quality of subsequent litchi planting management.

[0131] The second aspect of the present invention provides a resource scheduling configuration system for litchi pest control, such as Figure 4 As shown, the resource scheduling configuration system applied to litchi pest control includes a memory 41 and a processor 42, wherein the memory 41 stores a resource scheduling configuration method program applied to litchi pest control, and when the resource scheduling configuration method program applied to litchi pest control is executed by the processor 42, any one of the steps of the resource scheduling configuration method applied to litchi pest control is implemented.

[0132] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A resource scheduling configuration method applied to litchi pest control, characterized in that: The following steps are involved: S102: Obtaining control requirements for litchi pest species, constructing a dynamic activity prediction model for litchi pest species, predicting pest outbreak frequencies based on control requirements in the dynamic activity prediction model, and obtaining sub-activity trend transition prediction distributions for litchi pest species at consecutive future seasonal time series nodes; S104: determining the activity transition scale of litchi pest species transition to each future season time series node according to the survival curve layout field created by the sub-activity trend transition prediction distribution, analyzing the prevention and control resources required for the sub-planting area based on the activity transition scale, and generating a prevention and control analysis result; S106: If the analysis result of the prevention and control resources shows that static attribute prevention and control resources are needed, a dynamic invasion range of the litchi pest-invaded sub-planting area is established according to the dynamic invasion pathway of the litchi pest species, and a scheduling configuration of the static attribute prevention and control resources is performed based on the negotiation and membership operation between the dynamic invasion range and the global static prevention and control perception area to generate a first scheduling configuration plan; S108: If the analysis result of the prevention and control resources shows that dynamic attribute prevention and control resources are needed, the severity of the invasion of litchi pests on litchi leaves and fruits is determined based on the invasion rate, and the target dynamic attribute prevention and control resources with the maximum fit are dispatched for prevention and control according to the invasion severity, and a second scheduling configuration plan is generated; S110: Generate a normal-alert control area pattern through hash dislocation calculation analysis of the invasion degree density map and allocate control scheduling priorities according to the optimal control time and space nodes, and plan the optimal scheduling path for control resources based on the control scheduling priorities to improve the scheduling efficiency of litchi pest control resources.

2. A resource scheduling configuration method for litchi pest control according to claim 1, characterized in that: The step S102 specifically includes the following steps: Obtaining a planting area map of a litchi garden and monitoring terminal layout points of the planting area map, dividing the planting area map into a plurality of sub-planting areas based on the monitoring terminal layout points, using a monitoring terminal to capture real-time image data of each sub-planting area in real time and analyzing and extracting the contained feature information, so as to determine the types of litchi pests existing in each sub-planting area; Retrieving the monitoring logs of the corresponding monitoring terminals in each sub-planting area according to the monitoring terminal layout points, and extracting several frames of historical activity image data of the litchi pest species in each sub-planting area in a predetermined seasonal time sequence through the monitoring logs; Introducing an E-GARCH algorithm, constructing an E-GARCH model for each sub-planting area in the E-GARCH algorithm based on the several frames of historical active image data, and presetting an error term for the activity of litchi pest species in each sub-planting area according to each of the E-GARCH models; Creating a maximum likelihood function of the activity of litchi pest species in each sub-planting area based on the error term, performing autocorrelation validation training on the residuals of the E-GARCH model of each sub-planting area based on the maximum likelihood function, and finally generating a dynamic activity prediction model of litchi pest species in each sub-planting area; Obtain the prevention and control requirements of litchi pest types, preset the future season time series according to the prevention and control requirements, obtain the pest outbreak frequency of litchi pest types in the future season time series through the big data network, and discretize the future season time series into multiple continuous future season time series nodes based on the pest outbreak frequency; The dynamic activity prediction model is used to predict the activity transition trend of litchi pest species in each sub-planting area through future seasonal time series, and the activity transition trend prediction results are obtained. Finally, the sub-activity transition trend prediction distribution of litchi pest species at every two consecutive future seasonal time series nodes is separated from the activity transition trend prediction results.

3. A resource scheduling configuration method for litchi pest control according to claim 1, characterized in that: The step S104 specifically includes the following steps: Obtaining a planting scale coefficient of each sub-planting area according to a planting area map of a litchi orchard, and presetting a field boundary constraint threshold of each sub-planting area based on the planting scale coefficient; Construct a survival curve domain architecture based on future seasonal time series nodes and planting scale coefficients, expand and constrain the domain boundary of the survival curve domain architecture of each sub-planting area based on the domain boundary constraint threshold, and stop expanding the constraint until the current domain boundary value in the process of expanding the constraint meets the corresponding domain boundary constraint threshold; According to the distribution of the predicted transition trend of sub-activities, the survival curve layout is fitted and drawn in the survival curve domain framework completed with the expanded constraints, and the survival curve layout domain of each sub-planting area is obtained; The survival curve layout field is used to extract the survival probability fluctuation amplitude of the survival curve layout during the transition of the litchi pest species in each sub-planting area at the adjacent future season time series nodes, and the activity transition scale of the litchi pest species at each adjacent future season time series node is determined according to the survival probability fluctuation amplitude; Obtain the static and dynamic control resources that can be dispatched in the litchi orchard, and obtain the standard control scales corresponding to the types of litchi pests for each static and dynamic control resource, and quantify the activity transition scales between each adjacent future seasonal time series node to calculate and generate standardized statistics of the activity transition scales; A normal distribution table is constructed based on the standard prevention and control scale, and the critical interval of statistics for each static attribute prevention and control resource to reach the activity transition scale is extracted through the normal distribution table, which is defined as the static statistical critical interval; and the critical interval of statistics for each dynamic attribute prevention and control resource to reach the activity transition scale is extracted, which is defined as the dynamic statistical critical interval; If the standardized statistic is in the critical interval of the static statistic, it is determined that the sub-planting area requires static attribute prevention and control resources; if the standardized statistic is in the critical interval of the dynamic statistic, it is determined that the sub-planting area requires dynamic attribute prevention and control resources, and a prevention and control analysis result is generated.

4. A resource scheduling configuration method for litchi pest control according to claim 1, characterized in that: The step S106 specifically includes the following steps: If the results of the prevention and control resource analysis show that static-attribute prevention and control resources are needed, the dynamic invasion pathways of litchi pest species are separated from the dynamic activity prediction model based on the activity transition scale of litchi pest species at two consecutive future season time series nodes; Obtain a schematic diagram of the spatial layout of various static attribute prevention and control resources in the planting area map, and construct a dynamic invasion and diffusion equation for litchi pest invasion in the sub-planting area based on the activity transition scale of litchi pest species at two consecutive future season time series nodes and the dynamic invasion pathway; Creating a virtual domain of spatial layout of static attribute prevention and control resources in the planting area according to the spatial layout schematic diagram, solving the dynamic invasion and diffusion equation to generate a number of diffusion wave values ​​of litchi pest species invasion; By performing dynamic invasion fitting of a plurality of the diffusion wave values ​​in a virtual field of spatial layout, a dynamic virtual invasion model of the litchi pest species in the sub-planting area is generated, and a dynamic invasion range of the dynamic virtual invasion model is obtained; Obtain the preset static control range of each static attribute control resource, set the perception boundary threshold based on the preset static control range and the spatial layout schematic diagram, plan the global perception area of ​​all static attribute control resources for the planting area map in the spatial layout virtual field until the area boundary value meets the perception boundary threshold, and generate a global static control perception area; A perception negotiation area threshold is preset, and the global static prevention and control perception area is divided into M sub-perception areas according to the spatial layout diagram. If a dynamic invasion range and a sub-perception area generate a negotiation area and the area value of the negotiation area is greater than the perception negotiation area threshold, the sub-perception area is marked as a suspicious sub-perception area; A clustering calculation is performed on the preset static prevention and control range to which the suspicious sub-perception area belongs to obtain a clustering result. According to the clustering result, the static attribute prevention and control resources belonging to the suspicious sub-perception area are determined and dispatched and configured in the sub-planting area for prevention and control, thereby obtaining a first dispatching configuration plan.

5. A resource scheduling configuration method for litchi pest control according to claim 1, characterized in that: The step S108 specifically includes the following steps: If the analysis result of prevention and control resources shows that dynamic attribute prevention and control resources are needed, the prevention and control log of the litchi orchard is used to obtain the invasion rate of litchi pest species on litchi leaves and fruits in the sub-planting area when the corresponding activity transition scale occurs between each future seasonal time series node; A vector autoregression model is constructed based on the corresponding activity transition scale of litchi pest species in the sub-planting area between each future seasonal time series node, and the lag term and coefficient matrix of the vector autoregression model are obtained; A unit shock is set according to the invasion rate corresponding to each activity transition scale to obtain multiple unit shocks, and the multiple unit shocks are applied one by one to the vector autoregression model to recursively iterate the lag term and coefficient matrix on each future seasonal time series node, and a series of impulse response functions are obtained through recursive iteration; Obtain the control strategies for each dynamic attribute control resource in the litchi orchard and the established severity of the attack that each control strategy can control. Determine the severity of the attack of the litchi pest species on the leaves and fruits of the litchi in the sub-planting area at each future seasonal time series node based on a series of impulse response functions. By density estimation of each invasion severity, a density map of the invasion degree of litchi pest types in the sub-planting area is constructed, and a density color index between two consecutive future season time series nodes is extracted according to the invasion degree density map, and a predetermined density color index interval that can be controlled by each prevention and control strategy is set based on the predetermined invasion severity; Only one or more dynamic attribute control resources that can be executed corresponding to the control strategy whose density color index is within the predetermined density color index range are extracted and marked as the target dynamic attribute control resource. At this time, the actual topographic and geomorphic features of the sub-planting area and the preset topographic and geomorphic feature set that one or more target dynamic attribute control resources are allowed to run are obtained; The degree of fit between the actual topographic and geomorphic features and each preset topographic and geomorphic feature in the preset topographic and geomorphic feature set is calculated one by one, and finally the target dynamic attribute control resource corresponding to the preset topographic and geomorphic feature with the maximum fit is extracted and configured for litchi pest control at the future seasonal time series nodes to obtain the second scheduling configuration plan.

6. A resource scheduling configuration method for litchi pest control according to claim 1, characterized in that: The step S110 specifically includes the following steps: Preset the warning invasion severity and warning hash misalignment function interval, globally evaluate the invasion severity of the sub-planting area based on the density color index of the invasion degree density map, obtain the global invasion severity, and calculate the hash misalignment function between the global invasion severity and the warning invasion severity of the sub-planting area; If the hash dislocation function is not within the warning hash dislocation function interval, the sub-planting area is marked as a normal control area; if the hash dislocation function is within the warning hash dislocation function interval, the sub-planting area is marked as a warning control area, generating a normal-warning control area pattern; Obtain the climate season information of the current litchi garden, and retrieve the best prevention and control time and space node of the first scheduling configuration scheme or the second scheduling configuration scheme for the global invasion severity of the current litchi garden in the big data network based on the climate season information and the actual topographic features; The necessary prevention and control time is estimated according to the global invasion severity, and the prevention and control response ratio of static attribute prevention and control resources and dynamic attribute prevention and control resources for the ordinary prevention and control area or the alert prevention and control area is calculated based on the necessary prevention and control time and the optimal prevention and control time and space nodes; Only the normal control area or alert control area corresponding to the highest control response ratio is extracted and actual control is performed to obtain the actual control duration. The necessary control duration is continuously updated according to the actual control duration to generate a new highest control response ratio. The control scheduling priority of the normal-alert control area pattern is allocated according to the new highest control response ratio. Get the current normal-alert control area pattern to display all normal control areas or alert control areas assigned to the required control scheduling priority, all marked as waiting control scheduling areas, and obtain the current scheduling base station map of various static control resources and dynamic control resources and the scheduling path map of the litchi garden; Taking the optimal control time-space node as the accurate scheduling node, the Dijkstra algorithm is introduced. Based on the pattern of the scheduling area to be controlled and the current scheduling base station map, the Dijkstra algorithm dynamically plans the optimal scheduling path on the scheduling path map when various static control resources and dynamic control resources reach the accurate scheduling node, so as to improve the scheduling efficiency of litchi pest control resources.

7. A resource scheduling configuration system for litchi pest control, characterized in that: The resource scheduling and configuration system applied to litchi pest control includes a memory and a processor, wherein the memory stores a resource scheduling and configuration method program applied to litchi pest control. When the resource scheduling and configuration method program applied to litchi pest control is executed by the processor, the steps of the resource scheduling and configuration method applied to litchi pest control as described in any one of claims 1 to 6 are implemented.

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