Dynamic Prediction-Based Smart Agriculture Resource Allocation Management Method and System

Abstract

The present invention relates to a method and system for intelligent agricultural resource allocation management based on dynamic prediction, belonging to the technical field of intelligent agricultural resource allocation management. Multiple abnormal points within the signal transmission range are obtained, and the signal transmission intensity values of the interference sources of the multiple abnormal points on the electronic fence device are predicted based on a convolutional neural network to obtain a redundant range set. The GIS software is used to transform the several best growth factor information and the multiple real-time environmental monitoring data and judge the overlap degree to obtain the protection range required for crop planting. Based on the particle swarm algorithm, the installation points of the redundant range set and the protection range required for crop planting are screened and integrated to obtain an optimized installation point diagram of the electronic fence device. The signal transmission channel distance of the installation point coordinate values is calculated and verified to obtain the final layout diagram of the electronic fence device. The present invention can reselect the installation position of the interfered electronic fence and improve the monitoring performance.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent agricultural resource allocation management, and particularly to an intelligent agricultural resource allocation management method and system based on dynamic prediction. Background Art

[0002] Smart agriculture combines traditional agriculture with advanced technologies to improve crop production, aquaculture management, and resource utilization efficiency. Smart agriculture can also apply advanced Internet of Things technologies and big data analysis to achieve real-time monitoring of farm management. As a monitoring configuration of smart agricultural resources, an electronic fence uses electronic technology to achieve regional boundary control and security protection. Based on wireless communication and sensing technologies, it can monitor and control the entry and exit of a specific area to provide security protection and behavior restrictions. However, due to the existence of various interference sources in the monitoring environment, such as electromagnetic wave interference, plant occlusion, metal interference, etc., the electronic fence may be interfered when receiving signals, resulting in problems such as signal transmission interruption and insensitive induction during the operation of the electronic fence, leading to inaccurate monitoring of the entry and exit of objects, and even paralysis of the electronic fence system, reducing the quality and accuracy of agricultural monitoring. Moreover, the signal transmission of interference sources is constantly changing, making it impossible to accurately avoid interference sources to select the best installation point for installing the electronic fence. There is an urgent need for a resource allocation management method to optimize the installation location of the electronic fence. Summary of the Invention

[0003] The present invention overcomes the deficiencies of the prior art and provides an intelligent agricultural resource allocation management method and system based on dynamic prediction.

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

[0005] The first aspect of the present invention provides an intelligent agricultural resource allocation management method based on dynamic prediction, including the following steps:

[0006] Obtain the layout position information of the electronic fence device, import the layout position information into the spatial distribution coordinate model to obtain the signal transmission range of the electronic fence device, and obtain a plurality of abnormal points within the signal transmission range;

[0007] Based on a convolutional neural network, predict the signal transmission intensity of the interference sources of the plurality of abnormal points on the electronic fence device to obtain a plurality of signal transmission intensity values, and constrain and combine the interference ranges according to the plurality of signal transmission intensity values to obtain a redundant range set;

[0008] Obtain several optimal growth factor information of crop varieties and a plurality of real-time environmental monitoring data, convert the several optimal growth factor information and the plurality of real-time environmental monitoring data through GIS software and judge the overlap degree to obtain a preset protection range;

[0009] The installation points of the redundant range set and the preset protection range are screened and integrated based on the particle swarm optimization algorithm to obtain an optimized installation point diagram of the electronic fence device;

[0010] Adjust and optimize according to the optimized installation point diagram of the electronic fence device to obtain the installation point coordinate values of each electronic fence device, calculate the signal transmission channel distance of the installation point coordinate values and verify it to obtain the final layout diagram of the electronic fence device.

[0011] Further, in a preferred embodiment of the present invention, the method for obtaining the layout position information of the electronic fence device, importing the layout position information into the spatial distribution coordinate model to obtain the signal transmission range of the electronic fence device, and obtaining a plurality of abnormal points within the signal transmission range specifically includes the following steps:

[0012] Obtain the image information of the total planned area of the current crop land, and perform feature recognition on the image information of the total planned area of the current crop land based on the convolutional neural network to obtain the layout position information of the electronic fence device;

[0013] Construct a spatial distribution coordinate model, and import the layout position information of the electronic fence device into the spatial distribution coordinate model to obtain the layout coordinate model of the electronic fence device;

[0014] Obtain the historical signal transmission fault information and signal transmission power parameters of the electronic fence device, import the signal transmission power parameters into the layout coordinate model for calculation to obtain the signal transmission range of the electronic fence device, define a number of abnormal points according to the historical signal transmission fault information, and embed the number of abnormal points into the layout coordinate model to obtain the distribution positions of the abnormal points;

[0015] Judge whether the distribution positions of the abnormal points are within the signal transmission range of the electronic fence device. If so, it indicates that there is an abnormality in the signal transmission path of the electronic fence device, and obtain a plurality of abnormal points within the signal transmission range.

[0016] Further, in a preferred embodiment of the present invention, the method for predicting the signal transmission intensity of the interference sources of the plurality of abnormal points on the electronic fence device based on the convolutional neural network to obtain a plurality of signal transmission intensity values, and constraining and combining the interference regions according to the plurality of signal transmission intensity values to obtain a redundant range set specifically includes the following steps:

[0017] Obtain the signal interference conditions for the electronic fence device under different preset signal interference source combinations through a big data network, construct a signal transmission intensity prediction model based on a convolutional neural network, import the signal interference conditions for the electronic fence device under different preset signal interference source combinations into the signal transmission intensity prediction model for training, and obtain a trained signal transmission intensity prediction model;

[0018] Obtain the signal interference source information of each abnormal point in the crop land, import the signal interference source information into the trained signal transmission intensity prediction model, and obtain multiple signal transmission intensity values;

[0019] Preset an interference threshold, and judge whether each signal transmission intensity value is less than the interference threshold. If it is less, the interference range of the interference source on signal transmission is large; if it is greater, the interference range of the interference source on signal transmission is small, and obtain a judgment result. Based on the signal transmission intensity value and the judgment result, constrain the interference range to obtain several interference ranges;

[0020] Combine the several interference ranges to obtain a signal transmission interference range combination set, and mark the signal transmission interference range combination set as a redundant range set.

[0021] Further, in a preferred embodiment of the present invention, obtaining several optimal growth factor information of crop varieties and a plurality of real-time environmental monitoring data, converting the several optimal growth factor information and the plurality of real-time environmental monitoring data through GIS software and judging the overlap degree to obtain a preset protection range, specifically including the following steps:

[0022] Obtain the land use planning drawing information of the current crop, and obtain the total planned area of the crop land according to the land use planning drawing information; wherein, the land use planning drawing information of the current crop includes crop planting planning area information and public area information outside the planting planning area;

[0023] Obtain the crop variety planted in the crop, obtain several optimal growth factor information of the crop variety based on a big data network, convert the total planned area through GIS software to obtain a coverage analysis space, convert the several optimal growth factor information to obtain several first polygons, and import the several first polygons into the coverage analysis space to generate a first coverage space;

[0024] Obtain a plurality of real-time environmental monitoring data through various sensors in the crop planting area, convert the plurality of real-time environmental monitoring data through GIS software to obtain a plurality of second polygons, and import the plurality of second polygons into the coverage analysis space to generate a second coverage space;

[0025] Perform overlapping analysis on the first coverage space and the second coverage space based on the polygon coverage analysis method to obtain the overlapping degree, and determine whether the overlapping degree is greater than the preset overlapping degree. If it is greater, the overlapping part is used as the protection area and all overlapping parts are integrated to obtain the preset protection range.

[0026] Further, in a preferred embodiment of the present invention, perform point selection and integration on the redundant range set and the preset protection range based on the particle swarm optimization algorithm to obtain the optimized installation point map of the electronic fence device, which specifically includes the following steps:

[0027] Obtain the range of the crop planting area, and exclude the range of the crop planting area and the redundant range set from the preset protection range to obtain the optimized installable range of the electronic fence device;

[0028] Divide the optimized installable range of the electronic fence device into several sub-layout areas according to the layout information of the multiple electronic fence devices, and perform point selection on each sub-layout area based on the particle swarm optimization algorithm and the signal transmission area of the electronic fence device;

[0029] Define the signal transmission area of each electronic fence device as a particle, allocate positions and velocities and set fitness values, adjust the particle positions according to the velocities and recalculate the fitness values of the new positions to obtain the globally optimal position and the individual optimal position, and adjust the particle velocities based on the globally optimal position and the individual optimal position. Finally, until the iteration times are reached, several installable points of each sub-layout area are obtained;

[0030] Sort and splice the several installable points of each sub-layout area to obtain an installable point sequence list, determine the sub-layout area to be optimized according to the multiple abnormal points within the signal transmission range, and import the sub-layout area to be optimized into the installable point sequence list for matching to obtain multiple optimal installation points;

[0031] Obtain the point information that does not need to be adjusted, and integrate the point information that does not need to be adjusted and the multiple optimal installation points to obtain the optimized installation point map of the electronic fence device.

[0032] Further, in a preferred embodiment of the present invention, perform adjustment and optimization according to the optimized installation point map of the electronic fence device to obtain the installation point coordinate values of each electronic fence device, calculate the signal transmission channel distances of the installation point coordinate values and perform verification to obtain the final layout map of the electronic fence device, which specifically includes the following steps:

[0033] Adjust and optimize the installation positions of the electronic fence in the current sub-layout area to be optimized according to the optimized installation position diagram of the electronic fence device, and obtain the optimized layout diagram of the electronic fence device;

[0034] Import the optimized layout diagram of the electronic fence device into the layout coordinate model of the electronic fence device to obtain the optimized layout coordinate model of the electronic fence device, and extract coordinate values from the optimized layout coordinate model of the electronic fence device to obtain the installation position coordinate values of each current electronic fence device;

[0035] Calculate the Manhattan distance between points based on the installation position coordinate values of each electronic fence device, construct a distance matrix, and import the installation position coordinate values of each current electronic fence device into the distance matrix for calculation to obtain multiple signal transmission channel distances;

[0036] Judge whether each signal transmission channel distance is greater than the preset signal transmission channel distance. If it is greater, extract the installation position corresponding to the signal transmission channel, and sequentially select new positions in the sub-layout area where the installation position is located for adjustment and optimization based on the list of optimizable point arrangements, and verify after re-arrangement until it is less than the preset signal transmission channel distance to obtain the final layout diagram of the electronic fence device.

[0037] The second aspect of the present invention provides a smart agriculture resource allocation management system based on dynamic prediction. The smart agriculture resource allocation management system based on dynamic prediction includes a memory and a processor. A program for the smart agriculture resource allocation management method based on dynamic prediction is stored in the memory. When the program for the smart agriculture resource allocation management method based on dynamic prediction is executed by the processor, the following steps are implemented:

[0038] Obtain the layout position information of the electronic fence device, import the layout position information into the spatial distribution coordinate model to obtain the signal transmission range of the electronic fence device, and obtain multiple abnormal points within the signal transmission range;

[0039] Predict the signal transmission intensity of the interference sources of multiple abnormal points on the electronic fence device based on a convolutional neural network to obtain multiple signal transmission intensity values, and constrain and combine the interference ranges according to the multiple signal transmission intensity values to obtain a redundant range set;

[0040] Obtain several optimal growth factor information of crop varieties and multiple real-time environmental monitoring data, convert the several optimal growth factor information and the multiple real-time environmental monitoring data through GIS software and judge the overlap degree to obtain a preset protection range;

[0041] The installation points of the redundant range set and the preset protection range are screened and integrated based on the particle swarm optimization algorithm to obtain an optimized installation point diagram of the electronic fence device;

[0042] Adjust and optimize according to the optimized installation point diagram of the electronic fence device to obtain the installation point coordinate values of each electronic fence device, calculate the signal transmission channel distance of the installation point coordinate values and verify it to obtain the final layout diagram of the electronic fence device.

[0043] Further, in a preferred embodiment of the present invention, the method for obtaining a plurality of optimal growth factor information of the crop variety and a plurality of real-time environmental monitoring data, converting and judging the overlap degree of the plurality of optimal growth factor information and the plurality of real-time environmental monitoring data through GIS software to obtain a preset protection range specifically includes the following steps:

[0044] Obtain the land use planning drawing information of the current crop, and obtain the total planned area of the crop land according to the land use planning drawing information; wherein, the land use planning drawing information of the current crop includes the crop planting planning area information and the public area information outside the planting planning area;

[0045] Obtain the crop variety planted in the crop, obtain a plurality of optimal growth factor information of the crop variety based on the big data network, convert the total planned area through GIS software to obtain a coverage analysis space, convert the plurality of optimal growth factor information to obtain a plurality of first polygons, and import the plurality of first polygons into the coverage analysis space to generate a first coverage space;

[0046] Obtain a plurality of real-time environmental monitoring data through various sensors in the crop planting area, convert the plurality of real-time environmental monitoring data through GIS software to obtain a plurality of second polygons, and import the plurality of second polygons into the coverage analysis space to generate a second coverage space;

[0047] Based on the polygon coverage analysis method, perform overlap analysis on the first coverage space and the second coverage space to obtain an overlap degree, judge whether the overlap degree is greater than a preset overlap degree, if it is greater, then the overlapping part is used as a protection area and all overlapping parts are integrated to obtain a preset protection range.

[0048] Further, in a preferred embodiment of the present invention, the method for screening and integrating the redundant range set and the preset protection range based on the particle swarm optimization algorithm to obtain an optimized installation point diagram of the electronic fence device specifically includes the following steps:

[0049] Obtain the range of the crop planting area, and exclude the range of the crop planting area and the redundant range set from the preset protection range to obtain the optimizable installation range of the electronic fence device;

[0050] Divide the optimizable installation range of the electronic fence device into several sub-layout areas according to the layout information of the multiple electronic fence devices, and perform point selection for each sub-layout area based on the particle swarm algorithm and the signal transmission area of the electronic fence device;

[0051] Define the signal transmission area of each electronic fence device as a particle, assign positions and velocities and set fitness values, adjust the particle positions according to the velocities and recalculate the fitness values of the new positions to obtain the globally optimal position and the individual optimal position, and adjust the particle velocities based on the globally optimal position and the individual optimal position. Finally, until the number of iterations is reached, several installable points for each sub-layout area are obtained;

[0052] Sort and splice the several installable points for each sub-layout area to obtain an installable point sequence list. Determine the sub-layout areas to be optimized according to the multiple abnormal points within the signal transmission range, and import the sub-layout areas to be optimized into the installable point sequence list for matching to obtain multiple optimal installation points;

[0053] Obtain the point information that does not need to be adjusted, and integrate the point information that does not need to be adjusted and the multiple optimal installation points to obtain the optimized installation point diagram of the electronic fence device.

[0054] Further, in a preferred embodiment of the present invention, adjusting and optimizing according to the optimized installation point diagram of the electronic fence device to obtain the installation point coordinate values of each electronic fence device, calculating and verifying the signal transmission channel distance of the installation point coordinate values to obtain the final layout diagram of the electronic fence device, specifically including the following steps:

[0055] Adjust and optimize the installation position of the electronic fence in the current sub-layout area to be optimized according to the optimized installation point diagram of the electronic fence device to obtain an optimized layout diagram of the electronic fence device;

[0056] Import the optimized layout diagram of the electronic fence device into the layout coordinate model of the electronic fence device to obtain an optimized layout coordinate model of the electronic fence device, and extract coordinate values from the optimized layout coordinate model of the electronic fence device to obtain the installation point coordinate values of each current electronic fence device;

[0057] Calculate the Manhattan distance between the installation point coordinates of each electronic fence device, construct a distance matrix, import the installation point coordinates of each current electronic fence device into the distance matrix for calculation, and obtain multiple signal transmission channel distances;

[0058] Determine whether each signal transmission channel distance is greater than the preset signal transmission channel distance. If it is greater, extract the installation point corresponding to the signal transmission channel, and based on the list of optimizable point arrangements, sequentially select new points in the sub-layout area where the installation point is located for adjustment and optimization. After re-arrangement, verify until it is less than the preset signal transmission channel distance to obtain the final layout diagram of the electronic fence device.

[0059] The present invention solves the technical defects existing in the background art, and the beneficial technical effects of the present invention are as follows:

[0060] Obtain multiple abnormal points within the signal transmission range, predict the signal transmission intensity of the interference sources of the multiple abnormal points on the electronic fence device based on a convolutional neural network to obtain multiple signal transmission intensity values, and combine and constrain the interference range according to the multiple signal transmission intensity values to obtain a redundant range set. Transform and judge the overlap degree of the several best growth factor information and the multiple real-time environmental monitoring data through GIS software to obtain the protection range required for crop planting. Screen and integrate the installation points of the redundant range set and the protection range required for crop planting based on the particle swarm algorithm to obtain the optimized installation point diagram of the electronic fence device. Calculate and verify the signal transmission channel distance of the installation point coordinates to obtain the final layout diagram of the electronic fence device. The present invention can dynamically predict the interference range of the interference source, so as to adjust the installation points of the electronic fence device to be optimized according to the prediction results, improve the signal transmission quality of the electronic fence device, and reduce the signal transmission abnormality rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0062] Figure 1 Shows the first method flow chart of the intelligent agricultural resource allocation management method based on dynamic prediction;

[0063] Figure 2 Shows the second method flow chart of the intelligent agricultural resource allocation management method based on dynamic prediction;

[0064] Figure 3 Shows the third method flowchart of the intelligent agricultural resource allocation management method based on dynamic prediction;

[0065] Figure 4 Shows the system framework diagram of the intelligent agricultural resource allocation management system based on dynamic prediction. Detailed implementation manners

[0066] In order to be able to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.

[0067] Many specific details are set forth in the following description in order to fully understand 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 by the specific embodiments disclosed below.

[0068] The first aspect of the present invention provides an intelligent agricultural resource allocation management method based on dynamic prediction, as Figure 1 shown, including the following steps:

[0069] S102: Obtain the layout position information of the electronic fence device, import the layout position information into the spatial distribution coordinate model to obtain the signal transmission range of the electronic fence device, and obtain a plurality of abnormal points within the signal transmission range;

[0070] S104: Based on the convolutional neural network, predict the signal transmission intensity of the interference sources of the plurality of abnormal points on the electronic fence device to obtain a plurality of signal transmission intensity values, and constrain and combine the interference ranges according to the plurality of signal transmission intensity values to obtain a redundant range set;

[0071] S106: Obtain several optimal growth factor information of the crop variety and a plurality of real-time environmental monitoring data, convert the several optimal growth factor information and the plurality of real-time environmental monitoring data through GIS software and judge the overlap degree to obtain a preset protection range;

[0072] S108: Based on the particle swarm algorithm, screen and integrate the installation points of the redundant range set and the preset protection range to obtain an optimized installation point diagram of the electronic fence device;

[0073] S110: Adjust and optimize according to the optimized installation point diagram of the electronic fence device to obtain the installation point coordinate values of each electronic fence device, calculate the signal transmission channel distance of the installation point coordinate values and verify it to obtain the final layout diagram of the electronic fence device.

[0074] Further, in a preferred embodiment of the present invention, the steps of obtaining the layout position information of the electronic fence device, importing the layout position information into the spatial distribution coordinate model to obtain the signal transmission range of the electronic fence device, and obtaining a plurality of abnormal points within the signal transmission range are specifically as follows:

[0075] Obtain the image information of the overall planned area of the current crop land, and perform feature recognition on the image information of the overall planned area of the current crop land based on a convolutional neural network to obtain the layout position information of the electronic fence device;

[0076] Construct a spatial distribution coordinate model, and import the layout position information of the electronic fence device into the spatial distribution coordinate model to obtain the layout coordinate model of the electronic fence device;

[0077] Obtain the historical signal transmission fault information and signal transmission power parameters of the electronic fence device, import the signal transmission power parameters into the layout coordinate model for calculation to obtain the signal transmission range of the electronic fence device, define a number of abnormal points according to the historical signal transmission fault information, and embed the number of abnormal points into the layout coordinate model to obtain the distribution positions of the abnormal points;

[0078] Judge whether the distribution positions of the abnormal points are within the signal transmission range of the electronic fence device. If so, it indicates that there is an abnormality in the signal transmission path of the electronic fence device, and obtain a plurality of abnormal points within the signal transmission range.

[0079] It should be noted that the image information of the current total crop land planning area includes the image information of the crop planting planning area and the public area outside the planting planning area. Based on the convolutional neural network, the characteristics of the electronic fence devices in the image information are identified to obtain the layout position information of the electronic fence devices, and a layout coordinate model of the electronic fence devices is constructed. If it is necessary to know whether there is an abnormality in the signal transmission of the electronic fence devices, it is necessary to judge according to the historical signal transmission fault information of the electronic fence devices. The historical signal transmission fault information is the record information of the abnormal signal transmission and faults that occurred during the previous use of the electronic fence devices, excluding the fault records of the electronic fence devices themselves. The signal transmission power parameter is the signal emission power parameter of the electronic fence devices. Based on this power parameter, the signal transmission range of each electronic fence device can be determined. Each record included in the historical signal transmission fault information is defined as several abnormal points, and it is judged whether the distribution position of the abnormal points is within the signal transmission range of the electronic fence devices. If so, it means that there is an abnormality in the signal transmission path of the electronic fence devices. This method can judge the signal transmission abnormality of the electronic fence devices according to the historical signal transmission fault records and obtain the abnormal points, so as to formulate corresponding installation optimization schemes to solve the interference problems affecting the operation of the electronic fence and make the electronic fence play a greater monitoring performance.

[0080] Further, in a preferred embodiment of the present invention, the interference sources of the multiple abnormal points are used to predict the signal transmission intensity of the electronic fence devices based on the convolutional neural network, obtaining multiple signal transmission intensity values, and the interference regions are constrained and combined according to the multiple signal transmission intensity values to obtain a redundant range set, as Figure 2 shown, which specifically includes the following steps:

[0081] S202: Obtain the signal interference conditions for the electronic fence devices under different preset signal interference source combinations through the big data network, construct a signal transmission intensity prediction model based on the convolutional neural network, and import the signal interference conditions for the electronic fence devices under the different preset signal interference source combinations into the signal transmission intensity prediction model for training to obtain a trained signal transmission intensity prediction model;

[0082] S204: Obtain the signal interference source information of each abnormal point in the crop land, and import the signal interference source information into the trained signal transmission intensity prediction model to obtain multiple signal transmission intensity values;

[0083] S206: Preset an interference threshold, judge whether each signal transmission intensity value is less than the interference threshold. If it is less, the interference range of the interference source on the signal transmission is large. If it is greater, the interference range of the interference source on the signal transmission is small, obtaining a judgment result. Constraining the interference range based on the signal transmission intensity value and the judgment result to obtain several interference ranges;

[0084] S208: Combine the several interference ranges to obtain a set of combined signal transmission interference ranges, and label the set of combined signal transmission interference ranges as a redundant range set.

[0085] It should be noted that the different preset signal interference source combinations include combinations of various signal interference sources such as electromagnetic wave interference, plant occlusion, and metal product interference. Since the influence degree of each interference source on the signal transmission of the electronic fence device is different, and the interference signals of the interference sources are dynamically transmitted, it is necessary to first dynamically predict the interference degree of different interference source combinations on the signal transmission intensity of the electronic fence device, determine the interference range, construct a trained signal transmission intensity prediction model based on a convolutional neural network, import the signal interference source information of each abnormal point into the prediction model for prediction, and obtain the signal transmission intensity value of each abnormal point. The higher the signal transmission intensity value, the lower the signal interference degree. Therefore, the interference degree of each signal transmission intensity value can be judged by setting an interference threshold. If each signal transmission intensity value is less than the interference threshold, the interference range of the interference source on the signal transmission is large, otherwise the interference range is small. This is used as the judgment result output, and the interference range of each interference source combination is constrained based on the signal intensity value and the judgment result to obtain several interference ranges, and finally the interference ranges are integrated as a redundant range set. Through this method, the signal transmission intensity value of the electronic fence device under the interference source combination can be dynamically predicted, so as to constrain the interference range according to this intensity value, provide a premise for avoiding the interference source when reselecting the installation location, effectively enable the electronic fence to avoid the interference source, and improve the monitoring quality.

[0086] Further, in a preferred embodiment of the present invention, obtaining several optimal growth factor information of the crop variety and a plurality of real-time environmental monitoring data, converting the several optimal growth factor information and the plurality of real-time environmental monitoring data through GIS software and judging the overlap degree to obtain a preset protection range, specifically includes the following steps:

[0087] Obtain the land use planning drawing information of the current crop, and obtain the total planned area of the crop land according to the land use planning drawing information; wherein, the land use planning drawing information of the current crop includes the crop planting planning area information and the public area information outside the planting planning area;

[0088] Obtain the crop variety planted in the crop, obtain several optimal growth factor information of the crop variety based on the big data network, convert the total planned area through GIS software to obtain a coverage analysis space, convert the several optimal growth factor information to obtain several first polygons, and import the several first polygons into the coverage analysis space to generate a first coverage space;

[0089] Obtain multiple real-time environmental monitoring data through various sensors in the crop planting area, convert the multiple real-time environmental monitoring data through GIS software to obtain multiple second polygons, and import the multiple second polygons into the coverage analysis space to generate a second coverage space;

[0090] Based on the polygon coverage analysis method, perform an overlap analysis on the first coverage space and the second coverage space to obtain an overlap degree, and determine whether the overlap degree is greater than a preset overlap degree. If it is greater, the overlapping part is used as a protection area and all overlapping parts are integrated to obtain a preset protection range.

[0091] It should be noted that the main purpose of installing the electronic fence device is to protect a certain item or area from foreign intrusion. If it is used in agriculture, it can protect the crop planting area. The electronic fence needs to be installed within a certain protection range according to the actual planting area situation and the actual land use plan of the crops, rather than expanding the range arbitrarily. Therefore, it is necessary to obtain the total planned area protection range of the current crop land. The polygon coverage analysis is a method of overlapping and combining the ranges of multiple geographical elements, which can perform spatial analysis and matching on the most suitable growth conditions of plants and the surrounding environment, and find the overlapping range as the protection area. First, create a coverage analysis space for the total planned area of the current crop land through GIS software. The optimal growth factor information includes factors such as optimal light information, optimal temperature information, and optimal soil quality information. The real-time environmental monitoring data includes data such as real-time light data, real-time humidity data, and real-time pest and disease data. Convert the optimal growth factor information and the real-time environmental monitoring data into polygons and import them into the coverage analysis space to compare the polygon overlap degrees of the two, and determine whether the overlap degree is greater than the preset overlap degree. If it is greater, the overlapping part is used as the protection area and all overlapping parts are integrated to obtain the protection range required for crop planting. Through this method, the working range of the electronic fence device can be delimited, making the installation of the electronic fence device more reasonable and ensuring that the installation points do not exceed the self-planned range of the crop land.

[0092] Further, in a preferred embodiment of the present invention, the redundant range set and the preset protection range are screened and integrated based on the particle swarm algorithm to obtain an optimized installation point diagram of the electronic fence device, as Figure 3 shown, specifically including the following steps:

[0093] S302: Obtain the range of the crop planting area, and exclude the range of the crop planting area and the redundant range set from the preset protection range to obtain an optimized installable range for the electronic fence device;

[0094] S304: dividing the optimal installation range of the electronic fence device into a plurality of sub-layout areas according to the layout information of the plurality of electronic fence devices, and performing point screening on each of the sub-layout areas based on a particle swarm algorithm and a signal transmission area of ​​the electronic fence device;

[0095] S306: defining the signal transmission area of ​​each electronic fence device as a particle, assigning a position and a speed and setting a fitness value, adjusting the particle position according to the speed and recalculating the fitness value of the new position, obtaining a global optimal position and an individual optimal position, adjusting the particle speed based on the global optimal position and the individual optimal position, and finally obtaining a number of installable points in each sub-layout area until the number of iterations is reached;

[0096] S308: sorting and splicing a plurality of installable points in each sub-layout area to obtain an installable point sequence list, determining a sub-layout area to be optimized according to the plurality of abnormal points within the signal transmission range, importing the sub-layout area to be optimized into the installable point sequence list for matching, and obtaining a plurality of optimal installation points;

[0097] S310: Acquire point information that does not require adjustment, integrate the point information that does not require adjustment and the multiple optimal installation points, and obtain an installation point optimization map of the electronic fence device.

[0098] It should be noted that within the protection range, all interference ranges and crop planting area ranges are areas where electronic fence devices cannot be installed, so they need to be eliminated within the protection range. The remaining area is the installation optimization area of ​​the electronic fence device, among which there are several points that can be installed within the range. If one is randomly selected for optimization installation, it cannot guarantee the normal operation or optimal performance of the electronic fence device. Therefore, it is necessary to select the best installation optimization point for each device. First, the protection range is divided into several sub-layout areas according to the original layout position of the electronic fence device, and the points of each sub-layout area are screened based on the particle swarm algorithm. The particle swarm algorithm is a random optimization algorithm based on swarm intelligence. It simulates the movement and communication of particles in the search space to find multiple optimal solutions. At the same time, it considers whether the signal transmission area of ​​the electronic fence device intersects with the interference area. When several optimal optimization points are obtained, the points of each sub-layout area are sorted into a sequence table. Finally, the optimal point is selected according to the sub-layout area where each abnormal point is located, and combined with the points that do not need to be optimized to form an installation point optimization map of the electronic fence device. Through this method, the best installation optimization point can be selected for the area where the abnormal point is located within the protection range, and an installation optimization map can be generated, so that the electronic fence equipment can play the best working performance, improve the signal transmission quality and anti-interference ability, and have high reliability.

[0099] Further, in a preferred embodiment of the present invention, the installation position of each electronic fence device is adjusted and optimized according to the optimized installation position diagram of the electronic fence device, the signal transmission channel distance of the installation position coordinate value is calculated and verified, and the final layout diagram of the electronic fence device is obtained, which specifically includes the following steps:

[0100] Adjust and optimize the installation position of the electronic fence in the current sub-layout area to be optimized according to the optimized installation position diagram of the electronic fence device, and obtain the optimized layout diagram of the electronic fence device;

[0101] Import the optimized layout diagram of the electronic fence device into the layout coordinate model of the electronic fence device, obtain the optimized layout coordinate model of the electronic fence device, and extract the coordinate values in the optimized layout coordinate model of the electronic fence device to obtain the installation position coordinate value of each current electronic fence device;

[0102] Based on the installation position coordinate value of each electronic fence device, calculate the Manhattan distance between points, construct a distance matrix, and import the installation position coordinate value of each current electronic fence device into the distance matrix for calculation to obtain multiple signal transmission channel distances;

[0103] Judge whether each signal transmission channel distance is greater than the preset signal transmission channel distance. If it is greater, extract the installation position corresponding to the signal transmission channel, and sequentially select new points in the sub-layout area where the installation position is located for adjustment and optimization based on the list of optimizable point sorting, and verify after re-arrangement until it is less than the preset signal transmission channel distance to obtain the final layout diagram of the electronic fence device.

[0104] It should be noted that after obtaining the optimized installation point diagram of the electronic fence device, the position of the current electronic fence device can be adjusted for installation to solve the interference problem of the interference source. However, the signal transmission channel distance between each electronic fence device is fixed. If it exceeds the predetermined range, it will not be able to connect and work properly. After optimization, a new layout diagram of the electronic fence device is obtained. The layout diagram is imported into the layout coordinate model of the electronic fence device to obtain the coordinate values of each optimized point. Then, a distance matrix is established by calculating the Manhattan distance between the coordinate values of each point. The distance matrix can accurately obtain the signal transmission channel distance between each electronic fence device. Then, it is judged whether the signal transmission channel distance between each adjusted electronic fence device is greater than the preset distance. If it is greater, it means that the optimized point does not meet the requirements, resulting in an excessive signal transmission channel distance between the devices. It is necessary to discard the current best installation point and reselect the second best installation point in the sequence list for verification until all points meet the requirements and then integrate all points to obtain the final layout diagram for output. This method can verify whether the signal transmission channel distance between the optimized electronic fence devices meets the preset requirements, ensure that the signal transmission after the device installation optimization maintains normal operation, and improve the operation quality and work efficiency after the device interference optimization.

[0105] In addition, the intelligent agricultural resource allocation management method based on dynamic prediction further includes the following steps:

[0106] Construct a three-dimensional model of the final layout of the electronic fence according to the final layout diagram, and simulate the signal transmission of the electronic fence based on the three-dimensional model of the final layout of the electronic fence to obtain the actual duration of the signal simulated transmission;

[0107] Preset the ideal signal transmission duration, calculate the difference between the actual durations of multiple groups of the signal simulated transmission and the ideal signal transmission duration to obtain the signal transmission delay value, and judge whether the delay value is greater than the preset delay value. If it is greater, calculate the deviation threshold between the delay value and the preset delay value;

[0108] Obtain several optimization solutions for signal transmission delay based on the big data network, integrate the several solutions to generate an optimization solution set, import the deviation threshold into the optimization solution set for matching to obtain the initial delay optimization solution;

[0109] Construct a score evaluation system, import the initial optimization solution into the score evaluation system for scoring to obtain the evaluation score, and judge whether the evaluation score is greater than the preset evaluation score. If it is greater, set the initial delay optimization solution as the final delay optimization solution and upload it to the maintenance log of the electronic fence device.

[0110] It should be noted that when the electronic fence device is in use, due to factors such as transmission distance, transmission rate, and network congestion, signal transmission delays occur, resulting in delayed alarms when the electronic fence device monitors the entry and exit of animals or people, greatly reducing the monitoring quality of the electronic fence. This method can determine whether there is a signal transmission delay in the electronic fence device, and match the corresponding optimal optimization plan according to the delay result, efficiently and quickly solve problems such as slow signal transmission and insensitive monitoring signals caused by the delay, improve the monitoring quality of the electronic fence, reduce the error incidence rate, and have high reliability.

[0111] The second aspect of the present invention provides a smart agriculture resource allocation management system based on dynamic prediction. The smart agriculture resource allocation management system based on dynamic prediction includes a memory 41 and a processor 42. The memory 41 stores a program for the smart agriculture resource allocation management method based on dynamic prediction. When the program for the smart agriculture resource allocation management method based on dynamic prediction is executed by the processor 42, as Figure 4 shown, the following steps are implemented:

[0112] Obtain the layout position information of the electronic fence device, import the layout position information into the spatial distribution coordinate model to obtain the signal transmission range of the electronic fence device, and obtain multiple abnormal points within the signal transmission range;

[0113] Based on a convolutional neural network, predict the signal transmission intensity of the interference sources of multiple abnormal points on the electronic fence device to obtain multiple signal transmission intensity values, and constrain and combine the interference ranges according to the multiple signal transmission intensity values to obtain a redundant range set;

[0114] Obtain several optimal growth factor information of crop varieties and multiple real-time environmental monitoring data, convert and judge the overlap degree of the several optimal growth factor information and the multiple real-time environmental monitoring data through GIS software to obtain a preset protection range;

[0115] Based on the particle swarm algorithm, screen and integrate the installation points of the redundant range set and the preset protection range to obtain an optimized installation point diagram of the electronic fence device;

[0116] Adjust and optimize according to the optimized installation point diagram of the electronic fence device to obtain the installation point coordinate values of each electronic fence device, calculate the signal transmission channel distance of the installation point coordinate values and verify it to obtain the final layout diagram of the electronic fence device.

[0117] Further, in a preferred embodiment of the present invention, obtaining a plurality of optimal growth factor information of crop varieties and a plurality of real-time environmental monitoring data, converting the plurality of optimal growth factor information and the plurality of real-time environmental monitoring data through GIS software and judging the overlap degree to obtain a preset protection range, specifically including the following steps:

[0118] Obtain the land use planning drawing information of the current crop, and obtain the total planned area of the crop land according to the land use planning drawing information; wherein, the land use planning drawing information of the current crop includes crop planting planning area information and public area information outside the planting planning area;

[0119] Obtain the crop variety planted in the crop, obtain a plurality of optimal growth factor information of the crop variety based on the big data network, convert the total planned area through GIS software to obtain a coverage analysis space, convert the plurality of optimal growth factor information to obtain a plurality of first polygons, and import the plurality of first polygons into the coverage analysis space to generate a first coverage space;

[0120] Obtain a plurality of real-time environmental monitoring data through various sensors in the crop planting area, convert the plurality of real-time environmental monitoring data through GIS software to obtain a plurality of second polygons, and import the plurality of second polygons into the coverage analysis space to generate a second coverage space;

[0121] Based on the polygon coverage analysis method, perform overlap analysis on the first coverage space and the second coverage space to obtain an overlap degree, judge whether the overlap degree is greater than a preset overlap degree, if it is greater, then the overlapping part is used as a protection area and all overlapping parts are integrated to obtain a preset protection range.

[0122] Further, in a preferred embodiment of the present invention, performing point selection and integration on the redundant range set and the preset protection range based on the particle swarm algorithm to obtain an optimized installation point map of the electronic fence device, specifically including the following steps:

[0123] Obtain the range of the crop planting area, remove the range of the crop planting area and the redundant range set from the preset protection range to obtain an optimized installable range of the electronic fence device;

[0124] Divide the optimized installable range of the electronic fence device into several sub-layout areas according to the layout information of the plurality of electronic fence devices, and perform point selection on each sub-layout area based on the particle swarm algorithm and the signal transmission area of the electronic fence device;

[0125] Define the signal transmission area of each of the electronic fence devices as a particle, assign positions and velocities, and set fitness values. Adjust the particle positions according to the velocities and recalculate the fitness values of the new positions to obtain the globally optimal position and the individual optimal position. Based on the globally optimal position and the individual optimal position, adjust the particle velocities, and finally, until the number of iterations is reached, obtain several installable points for each sub-layout area;

[0126] Sort and splice the several installable points for each sub-layout area to obtain an installable point sequence list. Determine the sub-layout areas to be optimized according to the multiple abnormal points within the signal transmission range, and import the sub-layout areas to be optimized into the installable point sequence list for matching to obtain multiple optimal installation points;

[0127] Obtain the point information that does not need to be adjusted, and integrate the point information that does not need to be adjusted and the multiple optimal installation points to obtain an optimized installation point diagram of the electronic fence device.

[0128] Further, in a preferred embodiment of the present invention, adjust and optimize according to the optimized installation point diagram of the electronic fence device to obtain the installation point coordinate values of each electronic fence device, calculate and verify the signal transmission channel distances of the installation point coordinate values to obtain the final layout diagram of the electronic fence device, which specifically includes the following steps:

[0129] Adjust and optimize the installation positions of the electronic fences in the current sub-layout area to be optimized according to the optimized installation point diagram of the electronic fence device to obtain an optimized layout diagram of the electronic fence device;

[0130] Import the optimized layout diagram of the electronic fence device into the layout coordinate model of the electronic fence device to obtain an optimized layout coordinate model of the electronic fence device, and extract coordinate values from the optimized layout coordinate model of the electronic fence device to obtain the installation point coordinate values of each current electronic fence device;

[0131] Calculate the Manhattan distance between the points based on the installation point coordinate values of each electronic fence device, construct a distance matrix, and import the installation point coordinate values of each current electronic fence device into the distance matrix for calculation to obtain multiple signal transmission channel distances;

[0132] Judge whether each signal transmission channel distance is greater than a preset signal transmission channel distance. If it is greater, extract the installation points corresponding to the signal transmission channel, and sequentially select new points for adjustment and optimization for the sub-layout area where the installation points are located based on the list of optimizable point sorting, and verify after rearrangement until it is less than the preset signal transmission channel distance to obtain the final layout diagram of the electronic fence device.

[0133] 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 within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for intelligent agricultural resource allocation management based on dynamic prediction, characterized in that It includes the following steps: Obtain the layout position information of the electronic fence device, import the layout position information into the spatial distribution coordinate model to obtain the signal transmission range of the electronic fence device, and obtain multiple abnormal points within the signal transmission range; Based on the convolutional neural network, predict the signal transmission intensity of the interference sources at multiple abnormal points on the electronic fence device to obtain multiple signal transmission intensity values, and constrain and combine the interference ranges according to the multiple signal transmission intensity values to obtain a redundant range set; Obtain several optimal growth factor information of the crop variety and multiple real-time environmental monitoring data, and use GIS software to transform and judge the overlap degree of the several optimal growth factor information and the multiple real-time environmental monitoring data to obtain a preset protection range; Based on the particle swarm algorithm, screen and integrate the installation points of the redundant range set and the preset protection range to obtain an optimized installation point diagram of the electronic fence device; Adjust and optimize according to the optimized installation point diagram of the electronic fence device to obtain the installation point coordinate values of each electronic fence device, calculate the signal transmission channel distance of the installation point coordinate values and verify it to obtain the final layout diagram of the electronic fence device; Among them, based on the convolutional neural network, predicting the signal transmission intensity of the interference sources at multiple abnormal points on the electronic fence device to obtain multiple signal transmission intensity values, and constraining and combining the interference ranges according to the multiple signal transmission intensity values to obtain a redundant range set, specifically includes the following steps: Obtain the signal interference conditions of the electronic fence device under different preset signal interference source combinations through the big data network, construct a signal transmission intensity prediction model based on the convolutional neural network, and import the signal interference conditions of the electronic fence device under different preset signal interference source combinations into the signal transmission intensity prediction model for training to obtain a trained signal transmission intensity prediction model; Obtain the signal interference source information of each abnormal point in the crop land, and import the signal interference source information into the trained signal transmission intensity prediction model to obtain multiple signal transmission intensity values; Preset an interference threshold, judge whether each signal transmission intensity value is less than the interference threshold. If it is less, the interference range of the interference source on the signal transmission is large. If it is greater, the interference range of the interference source on the signal transmission is small, obtain a judgment result, and constrain the interference range based on the signal transmission intensity value and the judgment result to obtain several interference ranges; Combine the several interference ranges to obtain a signal transmission interference range combination set, and mark the signal transmission interference range combination set as a redundant range set.

2. The method for intelligent agricultural resource allocation management based on dynamic prediction according to claim 1, wherein The obtaining of the layout position information of the electronic fence device, importing the layout position information into the spatial distribution coordinate model to obtain the signal transmission range of the electronic fence device, and obtaining multiple abnormal points within the signal transmission range specifically includes the following steps: Obtain the image information of the overall planned area of the current crop land, and perform feature recognition on the image information of the overall planned area of the current crop land based on the convolutional neural network to obtain the layout position information of the electronic fence device; Build a spatial distribution coordinate model, import the layout position information of the electronic fence device into the spatial distribution coordinate model to obtain the layout coordinate model of the electronic fence device; Obtain the historical signal transmission fault information and signal transmission power parameters of the electronic fence device, import the signal transmission power parameters into the layout coordinate model for calculation to obtain the signal transmission range of the electronic fence device, define the historical signal transmission fault information as several abnormal points, and embed the several abnormal points into the layout coordinate model to obtain the distribution positions of the abnormal points; Judge whether the distribution positions of the abnormal points are within the signal transmission range of the electronic fence device. If so, it indicates that there is an abnormality in the signal transmission path of the electronic fence device, and obtain multiple abnormal points within the signal transmission range.

3. The method for intelligent agricultural resource allocation management based on dynamic prediction according to claim 1, wherein Obtain several optimal growth factor information of the crop variety and multiple real-time environmental monitoring data, and use GIS software to convert and judge the overlap degree of the several optimal growth factor information and the multiple real-time environmental monitoring data to obtain the preset protection range, which specifically includes the following steps: Obtain the land use planning drawing information of the current crop, and obtain the total planned area of the crop land according to the land use planning drawing information; wherein, the land use planning drawing information of the current crop includes the crop planting planning area information and the public area information outside the planting planning area; Obtain the crop variety planted in the crop, obtain several optimal growth factor information of the crop variety based on the big data network, use GIS software to convert the total planned area to obtain the coverage analysis space, convert the several optimal growth factor information to obtain several first polygons, and import the several first polygons into the coverage analysis space to generate the first coverage space; Obtain multiple real-time environmental monitoring data through various sensors in the crop planting area, use GIS software to convert the multiple real-time environmental monitoring data to obtain multiple second polygons, and import the multiple second polygons into the coverage analysis space to generate the second coverage space; Based on the polygon coverage analysis method, perform an overlap analysis on the first coverage space and the second coverage space to obtain the overlap degree, judge whether the overlap degree is greater than the preset overlap degree. If so, the overlapping part is used as the protection area and all overlapping parts are integrated to obtain the preset protection range.

4. The method for intelligent agricultural resource allocation management based on dynamic prediction according to claim 1, wherein Based on the particle swarm algorithm, perform point selection and integration on the redundant range set and the preset protection range to obtain the optimized installation point diagram of the electronic fence device, which specifically includes the following steps: Obtain the range of the crop planting area, remove the range of the crop planting area and the redundant range set from the preset protection range to obtain the optimized installation range of the electronic fence device; Divide the optimized installation range of the electronic fence device into several sub-layout areas according to the layout information of multiple electronic fence devices, and perform point selection on each sub-layout area based on the particle swarm algorithm and the signal transmission area of the electronic fence device; Define the signal transmission area of each of the said electronic fence devices as particles, assign positions and velocities, and set fitness values. Adjust the particle positions according to the velocities and recalculate the fitness values of the new positions to obtain the globally optimal position and the individual optimal position. Based on the globally optimal position and the individual optimal position, adjust the particle velocities. Finally, until the number of iterations is reached, obtain several installable points for each sub-layout area; Sort and splice the several installable points for each sub-layout area to obtain an installable point sequence list. Determine the sub-layout areas to be optimized according to multiple abnormal points within the signal transmission range, and import the sub-layout areas to be optimized into the installable point sequence list for matching to obtain multiple optimal installation points; Obtain the point information that does not need to be adjusted, and integrate the point information that does not need to be adjusted and the multiple optimal installation points to obtain an optimized installation point diagram of the electronic fence devices.

5. The method for intelligent agricultural resource allocation management based on dynamic prediction according to claim 1, wherein Adjust and optimize according to the optimized installation point diagram of the electronic fence devices to obtain the installation point coordinate values of each electronic fence device, calculate the signal transmission channel distances of the installation point coordinate values and verify them to obtain the final layout diagram of the electronic fence devices. Specifically, it includes the following steps: Adjust and optimize the installation positions of the electronic fences in the currently to-be-optimized sub-layout area according to the optimized installation point diagram of the electronic fence devices to obtain an optimized layout diagram of the electronic fence devices; Import the optimized layout diagram of the electronic fence devices into the layout coordinate model of the electronic fence devices to obtain an optimized layout coordinate model of the electronic fence devices, and extract coordinate values in the optimized layout coordinate model of the electronic fence devices to obtain the installation point coordinate values of each current electronic fence device; Calculate the Manhattan distances between points based on the installation point coordinate values of each electronic fence device, construct a distance matrix, and import the installation point coordinate values of each current electronic fence device into the distance matrix for calculation to obtain multiple signal transmission channel distances; Judge whether each signal transmission channel distance is greater than the preset signal transmission channel distance. If it is greater, extract the installation points corresponding to the signal transmission channel, and sequentially select new points for adjustment and optimization of the sub-layout area where the installation points are located based on the list of sortable points for optimization. After re-arrangement and verification until it is less than the preset signal transmission channel distance, obtain the final layout diagram of the electronic fence devices.

6. The intelligent agricultural resource allocation management system based on dynamic prediction is characterized in that The intelligent agricultural resource allocation management system based on dynamic prediction includes a memory and a processor. The memory stores a program for the intelligent agricultural resource allocation management method based on dynamic prediction. When the program for the intelligent agricultural resource allocation management method based on dynamic prediction is executed by the processor, the following steps are implemented: Obtain the layout position information of the electronic fence devices, import the layout position information into the spatial distribution coordinate model to obtain the signal transmission range of the electronic fence devices, and obtain multiple abnormal points within the signal transmission range; Predict the signal transmission intensity of the interference sources of multiple said abnormal points on the electronic fence device based on a convolutional neural network, obtain multiple signal transmission intensity values, and constrain and combine the interference ranges according to the multiple signal transmission intensity values to obtain a redundant range set; Obtain several best growth factor information of crop varieties and multiple real-time environmental monitoring data, convert the several best growth factor information and the multiple real-time environmental monitoring data through GIS software and judge the overlap degree to obtain a preset protection range; Based on the particle swarm optimization algorithm, screen and integrate the installation points of the redundant range set and the preset protection range to obtain an optimized installation point diagram of the electronic fence device; Adjust and optimize according to the optimized installation point diagram of the electronic fence device to obtain the installation point coordinate values of each electronic fence device, calculate the signal transmission channel distance of the installation point coordinate values and verify it to obtain the final layout diagram of the electronic fence device; Among them, predicting the signal transmission intensity of the interference sources of multiple said abnormal points on the electronic fence device based on a convolutional neural network, obtaining multiple signal transmission intensity values, and constraining and combining the interference ranges according to the multiple signal transmission intensity values to obtain a redundant range set specifically includes the following steps: Obtain the signal interference conditions of the electronic fence device under different preset signal interference source combinations through a big data network, construct a signal transmission intensity prediction model based on a convolutional neural network, and import the signal interference conditions of the electronic fence device under different preset signal interference source combinations into the signal transmission intensity prediction model for training to obtain a trained signal transmission intensity prediction model; Obtain the signal interference source information of each abnormal point in the crop land, and import the signal interference source information into the trained signal transmission intensity prediction model to obtain multiple signal transmission intensity values; Preset an interference threshold, judge whether each signal transmission intensity value is less than the interference threshold. If it is less, the interference range of the interference source on signal transmission is large. If it is greater, the interference range of the interference source on signal transmission is small, obtain a judgment result, and constrain the interference range based on the signal transmission intensity value and the judgment result to obtain several interference ranges; Combine the several interference ranges to obtain a signal transmission interference range combination set, and mark the signal transmission interference range combination set as a redundant range set.

7. The intelligent agricultural resource allocation management system based on dynamic prediction according to claim 6, characterized in that, The step of obtaining several best growth factor information of crop varieties and multiple real-time environmental monitoring data, converting the several best growth factor information and the multiple real-time environmental monitoring data through GIS software and judging the overlap degree to obtain a preset protection range specifically includes the following steps: Obtain the land use planning drawing information of the current crop, and obtain the total planned area of the crop land according to the land use planning drawing information; among them, the land use planning drawing information of the current crop includes the crop planting planning area information and the public area information outside the planting planning area; Obtain the crop varieties planted in the farmland, obtain several optimal growth factor information of the crop varieties based on the big data network, transform the total planning area through GIS software to obtain a coverage analysis space, transform the several optimal growth factor information to obtain several first polygons, and import the several first polygons into the coverage analysis space to generate a first coverage space; Obtain multiple real-time environmental monitoring data through various sensors in the farmland planting area, transform the multiple real-time environmental monitoring data through GIS software to obtain multiple second polygons, and import the multiple second polygons into the coverage analysis space to generate a second coverage space; Based on the polygon coverage analysis method, perform overlapping analysis on the first coverage space and the second coverage space to obtain an overlapping degree, and determine whether the overlapping degree is greater than a preset overlapping degree. If it is greater, the overlapping part is used as a protection area and all overlapping parts are integrated to obtain a preset protection range.

8. The intelligent agricultural resource allocation management system based on dynamic prediction according to claim 6, characterized in that The point selection and integration of the redundant range set and the preset protection range are carried out based on the particle swarm optimization algorithm to obtain an optimized installation point diagram of the electronic fence device, which specifically includes the following steps: Obtain the range of the farmland planting area, and exclude the range of the farmland planting area and the redundant range set from the preset protection range to obtain an optimized installable range for the electronic fence device; According to the layout information of multiple electronic fence devices, divide the optimized installable range of the electronic fence device into several sub-layout areas, and perform point selection on each sub-layout area based on the particle swarm optimization algorithm and the signal transmission area of the electronic fence device; Define the signal transmission area of each electronic fence device as a particle, allocate positions and velocities and set fitness values, adjust the particle positions according to the velocities and recalculate the fitness values of the new positions to obtain the global optimal position and the individual optimal position, and adjust the particle velocities based on the global optimal position and the individual optimal position. Finally, until the iteration times are reached, several installable points for each sub-layout area are obtained; Sort and splice the several installable points for each sub-layout area to obtain an installable point sequence list, determine the sub-layout area to be optimized according to multiple abnormal points within the signal transmission range, and import the sub-layout area to be optimized into the installable point sequence list for matching to obtain multiple optimal installation points; Obtain the point information that does not need to be adjusted, and integrate the point information that does not need to be adjusted and the multiple optimal installation points to obtain an optimized installation point diagram of the electronic fence device.

9. The intelligent agricultural resource allocation management system based on dynamic prediction according to claim 6, wherein Adjust and optimize according to the optimized installation point diagram of the electronic fence device to obtain the installation point coordinate values of each electronic fence device, calculate the signal transmission channel distance of the installation point coordinate values and perform verification to obtain the final layout diagram of the electronic fence device, which specifically includes the following steps: Adjust and optimize the installation position of the electronic fence in the current sub-layout area to be optimized according to the optimized installation point diagram of the electronic fence device to obtain an optimized layout diagram of the electronic fence device; Import the optimized layout diagram of the electronic fence device into the layout coordinate model of the electronic fence device to obtain the optimized layout coordinate model of the electronic fence device, and extract coordinate values from the optimized layout coordinate model of the electronic fence device to obtain the installation point coordinate values of each current electronic fence device; Calculate the Manhattan distance between points based on the installation point coordinate values of each electronic fence device, construct a distance matrix, and import the installation point coordinate values of each current electronic fence device into the distance matrix for calculation to obtain multiple signal transmission channel distances; Determine whether each signal transmission channel distance is greater than the preset signal transmission channel distance. If it is greater, extract the installation point corresponding to the signal transmission channel, and sequentially select new points for adjustment and optimization in the sub-layout area where the installation point is located based on the list of sortable optimization points, and verify after re-arrangement until it is less than the preset signal transmission channel distance to obtain the final layout diagram of the electronic fence device.

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