Agricultural machinery scheduling method and device based on interval search, equipment and storage medium

Abstract

The invention provides an agricultural machinery scheduling method and device based on interval search, equipment and a storage medium, and the method comprises the steps: taking the number of agricultural machinery as a threshold value, and building a quadtree model corresponding to an area; after a place where crops need to be emergently harvested is determined, the number of needed agricultural machines is determined as a K value according to the area size of a to-be-harvested area; taking the position of the farmland to be harvested as a circle center, determining a search radius according to the number of agricultural machinery in an area corresponding to a node in the quadtree model, preliminarily determining a circular search area according to the position of the circle center and the search radius, and then calculating the spatial position of a circumscribed rectangle of the circular search area; and carrying out rapid search in the established quadtree model, and carrying out calculation according to the positions of all agricultural machinery in nodes related to a circumscribed rectangle, so as to find K nearest agricultural machinery for scheduling, thereby completing a rush-receiving task. The agricultural machinery scheduling method is used for solving the agricultural machinery scheduling problem, reducing the search time and improving the agricultural machinery scheduling efficiency, thereby meeting the requirements of agricultural production in various regions.

Description

Technical Field

[0001] The present invention relates to the technical field of data algorithms, and in particular to a farm machinery scheduling method based on interval search and a device applied to the method, and also relates to a computer device and a storage medium for implementing the above method. Background Art

[0002] With the continuous advancement of agricultural mechanization, the development of agricultural mechanization at a deeper level is becoming more and more urgent, and the socialized and intelligent services of agricultural machinery are becoming more and more common in rural areas. In the busy farming season for the management of the dispatched operations of agricultural machinery, agricultural machinery service organizations still lack effective technical monitoring means. There is no practical and effective monitoring and scheduling platform. Especially during the busy farming seasons such as summer harvest and autumn harvest, the supply-demand gap of agricultural machinery is relatively large. The operation distribution of agricultural machinery during the busy farming period is uneven, and the operation routes are not scientifically and effectively planned, which greatly affects the operation efficiency of agricultural machinery service organizations, and the production needs of farmers cannot be effectively met.

[0003] Bad weather often has a great impact on the harvesting of crops. In order to ensure that the crops are harvested in full, the relevant departments need to scientifically organize and optimize the emergency harvesting work, so that once a piece of crops is ripe, it can be harvested immediately, ensuring that the summer grain is harvested in full and quickly. This often requires organizing a large number of agricultural machinery to efficiently carry out cross-regional machine harvesting operations. To do a good job in the rush harvesting of crops, it is necessary to dynamically pay attention to the maturity of crops and weather conditions, and also to scientifically coordinate and dispatch agricultural machinery, and timely optimize the cross-regional operation plan of agricultural machinery.

[0004] In the current agricultural production process, the scheduling of agricultural machinery is mostly evaluated by an expert system or controlled through manual decision-making. There are few automatic scheduling systems or tools for intelligent scheduling decisions of agricultural machinery. In order to obtain the maximum productivity with the minimum consumption, complete the agricultural production process in the shortest time, and improve the utilization rate of resources at the same time, it is necessary to conduct in-depth and detailed research on the problem of agricultural machinery scheduling, and find a better algorithm to automatically schedule and control the operations of agricultural machinery. Therefore, for the problem of finding the K nearest agricultural machinery for scheduling for a specific point within a specified planar area, there is a lack of an algorithm that uses a quadtree to divide the nodes of the specified area, and there is a lack of consideration in terms of the effective search range, resulting in a high time complexity and a low search efficiency. Summary of the Invention

[0005] The present invention provides a farm machinery scheduling method, device, equipment and storage medium based on interval search. This method can solve the problem of farm machinery scheduling, meet the needs of agricultural production in various regions, improve the scheduling efficiency of agricultural machinery, reduce the search time, improve the work efficiency, thereby ensuring the full play of the operation efficiency of agricultural machinery and the social and economic benefits, and effectively promoting the further improvement of the level of agricultural mechanization.

[0006] In a first aspect, the present invention provides a farm machinery scheduling method based on interval search. The method includes:

[0007] Taking the number of farm machines as a threshold, a quadtree model corresponding to this area is established;

[0008] After determining the location where the crops need to be urgently harvested, determine the number of required farm machines as the K value according to the area size of the area to be harvested;

[0009] Taking the central position of the farmland to be harvested as the center of the circle, and determining the search radius based on the number of farm machines in the area corresponding to the node in the quadtree model where it is located. Initially determine a circular search area through the center position and the search radius, and then calculate the spatial position of the circumscribed rectangle of this circular search area;

[0010] Perform a rapid search in the established quadtree model according to this circumscribed rectangle, and perform distance parallel calculation based on the positions of all farm machines in the nodes involved in the circumscribed rectangle, so as to find the K nearest farm machines for scheduling to complete the rush harvest task.

[0011] According to the farm machinery scheduling method based on interval search provided by the present invention, the farthest distance from the central position of the harvested farmland to the boundary point of the rectangular area corresponding to its node in the quadtree is the distance from the central position to the farthest one of the four vertices of this rectangular area, which is used as the search radius.

[0012] According to the farm machinery scheduling method based on interval search provided by the present invention, after calculating the spatial position of the circumscribed rectangle, the four sides of this circumscribed rectangle are all tangent to the spatial area of the circle. Query the nodes in the quadtree according to the circumscribed rectangle, and find all leaf nodes with spatial overlap or partial area overlap as the search area, converting the KNN search problem into an interval search problem based on the quadtree.

[0013] According to the farm machinery scheduling method based on interval search provided by the present invention, the establishment of the quadtree model corresponding to this area includes:

[0014] Use the quadtree method to divide the spatial area layer by layer. First divide the initial spatial area into four sub-areas. If the number of points in the sub-area is greater than the pre-set threshold, then further divide this sub-area into four smaller sub-areas until the number of points in each sub-area does not exceed the pre-set threshold, so as to ensure that the distribution density of points in each leaf node area is relatively uniform.

[0015] According to a farm machinery scheduling method based on interval search provided by the present invention, during the tree construction process, information such as the boundary coordinates of the sub-region, the spatial positions and quantities of all points included are stored, and all points in the specified region are correspondingly divided into the regions corresponding to the leaf nodes of the quadtree to ensure that the distribution density of points in each node region is relatively uniform.

[0016] In a second aspect, the present invention also provides a farm machinery scheduling device based on interval search, including:

[0017] A quadtree unit for using the number of farm machinery as a threshold to establish a quadtree model corresponding to this region;

[0018] A node region unit for determining the location where crops need to be urgently harvested, and then determining the required number of farm machinery as the K value according to the area of the area to be harvested;

[0019] An outer circumscribed rectangle generation unit for using the central position of the farmland to be harvested as the center of the circle, and determining the search radius based on the number of farm machinery in the corresponding region of the node in the quadtree model where it is located. Initially determine a circular search region through the center position of the circle and the search radius, and then calculate the spatial position of the outer circumscribed rectangle of this circular search region;

[0020] A scheduling unit for quickly searching in the established quadtree model according to this outer circumscribed rectangle, and performing distance parallel calculation based on the positions of all farm machinery in the nodes involved in the outer circumscribed rectangle, so as to find the nearest K farm machinery for scheduling to complete the rush harvest task.

[0021] It can be seen that the present invention applies the improved interval search algorithm to farm machinery scheduling, adapts to various agricultural production realities, realizes the optimal scheduling of farm machinery, and enhances the production service efficiency of agricultural machinery. It can scientifically plan and schedule farm machinery, and timely optimize the cross-regional operation plan of farm machinery. The present invention is an algorithm for interval optimization based on prior experience, which accelerates the algorithm optimization process and improves the efficiency of finding the optimal solution to the farm machinery scheduling problem.

[0022] In addition, when the present invention searches and schedules farm machinery, by dividing the quadtree sub-nodes of the current region, the search range is subdivided into search grid units with a more uniform point distribution density, so as to query nodes in the quadtree according to the outer circumscribed rectangle of the search range, and find all leaf nodes with spatial overlap, which can effectively narrow the search range and improve the scheduling efficiency.

[0023] In a third aspect, the present invention also provides an electronic device, including:

[0024] A memory storing computer-executable instructions;

[0025] A processor configured to run the computer-executable instructions,

[0026] Among them, when the computer-executable instructions are run by the processor, the steps of any one of the above-mentioned agricultural machinery scheduling methods based on interval search are implemented.

[0027] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the steps of any one of the above-mentioned agricultural machinery scheduling methods based on interval search are implemented.

[0028] Thus, the present invention provides an electronic device and a storage medium for an agricultural machinery scheduling method based on interval search, which include: one or more memories, and one or more processors. The memory is used for storing program codes, intermediate data generated during program operation, storage of model output results, and storage of models and model parameters; the processor is used for the processor resources occupied by code operation and multiple processor resources occupied during model training.

[0029] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. Description of the Drawings

[0030] Figure 1 is a flowchart of an embodiment of an agricultural machinery scheduling method based on interval search according to the present invention.

[0031] Figure 2 is a schematic diagram of parallel establishment of a quadtree model in an embodiment of an agricultural machinery scheduling method based on interval search according to the present invention.

[0032] Figure 3 is a schematic diagram of calculating the position of the circumscribed rectangle of the search range in an embodiment of an agricultural machinery scheduling method based on interval search according to the present invention.

[0033] Figure 4 is a schematic diagram of determining the search range according to the circumscribed rectangle by spatial search in an embodiment of an agricultural machinery scheduling method based on interval search according to the present invention.

[0034] Figure 5 is a schematic diagram of the principle of an embodiment of an agricultural machinery scheduling device based on interval search according to the present invention. Detailed Embodiments

[0035] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present invention belong to the scope of protection of the present invention.

[0036] The K-Nearest Neighbor (KNN) classification algorithm is a theoretically mature method. Its idea is as follows: In the feature space, if most of the K nearest (i.e., the closest in the feature space) samples near a sample belong to a certain category, then this sample also belongs to this category. For the KNN problem or the K-nearest neighbor problem: Given a data set and a point, find the K data points in the data set that are closest to the given point, which has extensive application value in image classification, information acquisition, pattern recognition, etc.

[0037] See Figure 1 , a farm machinery scheduling method based on interval search, includes the following steps:

[0038] Step S1, take the number of farm machinery as the threshold and establish the corresponding quadtree model for this area; where the threshold is preset and is a value greater than 4 times the K value.

[0039] Step S2, after determining the location where crops need to be urgently harvested, determine the number of required farm machinery as the K value according to the area size of the area to be harvested;

[0040] Step S3, take the center position of the farmland to be harvested as the center of the circle, and determine the search radius according to the number of farm machinery in the corresponding area of the node in the quadtree model where it is located. Initially determine a circular search area through the center position and the search radius, and then calculate the spatial position of the circumscribed rectangle of this circular search area.

[0041] Step S4, perform a quick search in the established quadtree model according to this circumscribed rectangle, and perform distance parallel calculations based on the positions of all farm machinery in the nodes involved in the circumscribed rectangle, so as to find the K nearest farm machinery for scheduling to complete the rush harvest task.

[0042] In the above step S3, the farthest distance from the center position of the harvested farmland to the boundary point of the corresponding rectangular area of its node in the quadtree is the distance from the center position to the farthest vertex among the four vertices of this rectangular area, which is used as the search radius. Among them, draw a circle based on the search radius, and then determine the circumscribed rectangle of this circle.

[0043] After calculating the spatial position of the circumscribed rectangle, the four sides of this circumscribed rectangle are all tangent to the spatial area of the circle. Query the nodes in the quadtree according to the circumscribed rectangle, and find all leaf nodes with spatial overlap or partial area overlap as the search area, converting the KNN search problem into an interval search problem based on the quadtree.

[0044] In the above step S3, establishing the corresponding quadtree model for this area includes:

[0045] The spatial region is divided layer by layer using the quadtree method. First, the initial spatial region is divided into four sub-regions. If the number of points in a sub-region is greater than a pre-set threshold, then this sub-region is further divided into four smaller sub-regions until the number of points in each sub-region does not exceed the pre-set threshold, so as to ensure that the distribution density of points in each leaf node region is relatively uniform.

[0046] During the tree construction process, information such as the boundary coordinates of the sub-region, the spatial positions and quantities of all the contained points is stored. All the points in the specified region are correspondingly divided into the regions corresponding to the leaf nodes of the quadtree to ensure that the distribution density of points in each node region is relatively uniform.

[0047] Specifically, take the current region as the search scope and build a quadtree in parallel according to the set threshold. The spatial region is divided into four sub-regions layer by layer, and multiple threads are allocated to execute in parallel as needed. If the number of points in a sub-region is greater than the threshold, it is further divided into four smaller sub-regions until the number of points in each sub-region does not exceed the threshold. During the tree construction process, information such as the boundary coordinates of the sub-region, the storage positions and quantities of all the contained points is stored. Thus, all the points in the specified region are correspondingly divided into the regions corresponding to the leaf nodes of the quadtree, ensuring that the distribution density of points in each node region is relatively uniform, as Figure 2 shown.

[0048] Next, determine the leaf node where the central position of the farmland to be harvested is located: According to the spatial position of the central position of the farmland to be harvested, determine the leaf node in the quadtree it belongs to through parallel search, that is, determine the region of its leaf node, and determine the search node according to the number of data points contained in this node.

[0049] Then, perform an interval search based on the circumscribed rectangular region of a circle with the search radius. Draw a circle with the central position of the farmland to be harvested as the center and the maximum distance from the central position of the farmland to be harvested to the boundary points of this search node region as the search radius, and then calculate the spatial position of the circumscribed rectangle of the search scope, as Figure 3 shown. Perform a spatial search in parallel by layer in the quadtree for this rectangular region, that is, query according to the boundary coordinates, and go deeper layer by layer from the queried and matched nodes until the leaf nodes. All the leaf nodes that have spatial overlap with the circumscribed rectangle are used as the effective search scope, as Figure 4 shown.

[0050] Finally, calculate the distances to find K agricultural machines: Based on the search scope, use multiple threads to calculate the distances between all the points in this scope and the central position of the farmland to be harvested in parallel, and then sort by distance to find the K agricultural machines closest to the given point.

[0051] Further, regarding the description of the quadtree node threshold: The quadtree node threshold is a very important parameter in the quadtree data structure, which represents the maximum number of two-dimensional data points that can be stored in a quadtree leaf node. In the traditional quadtree construction process, when a new data point is inserted into a quadtree leaf node whose contained data points reach the quadtree node threshold, this quadtree leaf node will be split, generating four child leaf nodes, and the data points it originally contained will be respectively transferred and stored in its child leaf nodes. From another perspective, for any quadtree non-leaf node, the total number of data points contained in its descendant leaf nodes must be greater than the quadtree node threshold.

[0052] In a GPU-oriented quadtree, the setting of its node threshold is related to relevant applications. For example, when the quadtree is used for the KNN problem, the quadtree node threshold is required to be larger than K (it is recommended to set the threshold to be greater than 4 times the value of K), because this can ensure that when determining the search range according to the search radius, there is at least one non-leaf node within the range, thus ensuring that there are more than K data points within the range, from which K nearest neighbors can be selected.

[0053] In practical applications, to find the K nearest agricultural machines at the central position (small triangle) of the farmland to be harvested in a specified area, it includes the following steps:

[0054] (1) Build a quadtree for the current area according to the threshold to obtain Figure 2 the area division result shown.

[0055] (2) Determine that the position of the central position (small triangle) of the farmland to be harvested is in the area corresponding to node 18;

[0056] (3) Calculate the maximum distance from the central position of the farmland to be harvested to the boundary points of node 18, take it as the search radius, and then calculate the spatial position of the circumscribed rectangle of the search range, as Figure 3 shown; then perform a spatial search in parallel layer by layer according to this circumscribed rectangle, and search for all leaf node areas with spatial overlap as the effective search range, expressed as: nodes 3, 6, 9, 12, 14, 15, 17, 18, 19, 20.

[0057] (4) Calculate the distances between all points (small dots) in the search range and the central position (small triangle) of the farmland to be harvested in parallel, and find the K nearest agricultural machines to the position of the farmland to be harvested.

[0058] It can be seen that the present invention applies the improved interval search algorithm to agricultural machinery scheduling, adapts to various actual agricultural production situations, realizes the optimal scheduling of agricultural machinery, and enhances the production service efficiency of agricultural machinery. It can scientifically plan and dispatch agricultural machinery, and timely optimize the pre-plan for cross-regional operation of agricultural machinery. The present invention is an algorithm for interval optimization based on prior experience, which accelerates the optimization process of the algorithm and improves the efficiency of finding the optimal solution to the agricultural machinery scheduling problem. In addition, when searching and dispatching agricultural machinery, the present invention divides the current area into the sub-nodes of a quadtree, divides the search range into search grid units with a more uniform point distribution density, and then queries the nodes in the quadtree according to the circumscribed rectangle of the search range to find all leaf nodes with spatial overlap, which can effectively narrow the search range and improve the scheduling efficiency.

[0059] An embodiment of an agricultural machinery scheduling device based on interval search

[0060] As Figure 5 shown, this embodiment provides an agricultural machinery scheduling device based on interval search, including:

[0061] A quadtree unit 10, used to establish a quadtree model corresponding to this area with the number of agricultural machinery as the threshold;

[0062] A node area unit 20, used to determine the location where crops need to be urgently harvested, and determine the required number of agricultural machinery as the K value according to the area size of the area to be harvested;

[0063] A circumscribed rectangle generation unit 30, used to use the central position of the farmland to be harvested as the center of the circle, and determine the search radius according to the number of agricultural machinery in the corresponding area of the node in the quadtree model where it is located. Initially determine a circular search area through the center position and the search radius, and then calculate the spatial position of the circumscribed rectangle of this circular search area;

[0064] A scheduling unit 40, used to quickly search in the established quadtree model according to this circumscribed rectangle, perform distance parallel calculation according to the positions of all agricultural machinery in the nodes involved in the circumscribed rectangle, so as to find the nearest K agricultural machinery for scheduling to complete the rush harvest task.

[0065] In the circumscribed rectangle generation unit 30, the farthest distance from the central position of the harvested farmland to the boundary point of the corresponding rectangular area of the node in the quadtree where it is located is the distance from the central position to the farthest vertex among the four vertices of this rectangular area, which is used as the search radius.

[0066] After calculating the spatial position of the circumscribed rectangle, the four sides of this circumscribed rectangle are tangent to the spatial area of the circle. Query the nodes in the quadtree according to the circumscribed rectangle to find all leaf nodes with spatial overlap or partial area overlap as the search area, and convert the KNN search problem into an interval search problem based on the quadtree.

[0067] In the quadtree unit 10, a quadtree model corresponding to the area is established, including:

[0068] The spatial area is divided layer by layer in a quadtree manner. First, the initial spatial area is divided into four sub-areas. If the number of points in a sub-area is greater than a pre-set threshold, then the sub-area is further divided into four smaller sub-areas until the number of points in each sub-area does not exceed the pre-set threshold, so as to ensure that the distribution density of points in each leaf node area is relatively uniform.

[0069] During the tree construction process, information such as the boundary coordinates of the sub-areas, the spatial positions and quantities of all the included points is stored, and all the points in the specified area are correspondingly divided into the areas corresponding to the leaf nodes of the quadtree, so as to ensure that the distribution density of points in each node area is relatively uniform.

[0070] In one embodiment, an electronic device is provided. The electronic device may be a server. The computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements an agricultural machinery scheduling method based on interval search.

[0071] Those skilled in the art can understand that the structure of the electronic device shown in this embodiment is only a part of the structure related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than those shown in this embodiment, or combine some components, or have different component arrangements.

[0072] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps in the above method embodiments.

[0073] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0074] In addition, when the logical instructions in the above-mentioned memory are implemented in the form of a software functional unit and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. And the aforementioned storage medium includes: USB flash drive, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk, or optical disc, etc., which can store program codes.

[0075] Thus, it can be seen that the present invention provides an electronic device and a storage medium for an agricultural machinery scheduling method based on interval search, which include: one or more memories, and one or more processors. The memory is used for storing program codes, intermediate data generated during program operation, storage of model output results, and storage of models and model parameters; the processor is used for the processor resources occupied by code operation and multiple processor resources occupied during training the model.

[0076] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0077] The above implementation manners are only the preferred implementation manners of the present invention, and cannot be used to limit the scope of protection of the present invention. Any non-substantive changes and substitutions made by those skilled in the art on the basis of the present invention belong to the scope of protection required by the present invention.

Claims

1. An agricultural machinery scheduling method based on interval search, characterized in that Including: Taking the number of agricultural machines as a threshold, establish the corresponding quadtree model for this area; After determining the location where crops need to be urgently harvested, determine the number of agricultural machines required according to the area size of the area to be harvested as the K value; Taking the center position of the farmland to be harvested as the center of the circle, and determining the search radius based on the number of agricultural machines in the corresponding area of the node in the quadtree model where it is located. Initially determine a circular search area through the center position of the circle and the search radius, and then calculate the spatial position of the circumscribed rectangle of this circular search area; Perform a quick search in the established quadtree model according to this circumscribed rectangle, and perform distance parallel calculations based on the positions of all agricultural machines in the nodes involved in the circumscribed rectangle, so as to find the K nearest agricultural machines for scheduling to complete the rush harvest task.

2. The method according to claim 1, wherein: The farthest distance from the center position of the harvested farmland to the boundary point of the corresponding rectangular area of the quadtree node where it is located is the distance from the center position to the farthest vertex among the four vertices of this rectangular area, which is used as the search radius.

3. The method according to claim 2, wherein: After calculating the spatial position of the circumscribed rectangle, the four sides of this circumscribed rectangle are tangent to the spatial area of the circle. Query the nodes in the quadtree according to the circumscribed rectangle, and find all leaf nodes with spatial overlap or partial area overlap as the search area, and convert the KNN search problem into an interval search problem based on the quadtree.

4. The method according to claim 1, characterized in that, The establishment of the corresponding quadtree model for this area includes: Use the quadtree method to divide the spatial area layer by layer. First divide the initial spatial area into four sub-areas. If the number of points in the sub-area is greater than the pre-set threshold, then further divide this sub-area into four smaller sub-areas until the number of points in each sub-area does not exceed the pre-set threshold, so as to ensure that the distribution density of points in each leaf node area is relatively uniform.

5. The method according to claim 4, wherein: During the tree construction process, store information such as the boundary coordinates of the sub-area, the spatial positions and quantities of all points included, and divide all points in the specified area into the areas corresponding to the leaf nodes of the quadtree accordingly, so as to ensure that the distribution density of points in each node area is relatively uniform.

6. An agricultural machinery scheduling device based on interval search, characterized in that, Including: A quadtree unit for taking the number of agricultural machines as a threshold and establishing the corresponding quadtree model for this area; A node area unit for determining the location where crops need to be urgently harvested and then determining the number of agricultural machines required as the K value according to the area size of the area to be harvested; A circumscribed rectangle generation unit for taking the center position of the farmland to be harvested as the center of the circle and determining the search radius based on the number of agricultural machines in the corresponding area of the node in the quadtree model where it is located, initially determining a circular search area through the center position of the circle and the search radius, and then calculating the spatial position of the circumscribed rectangle of this circular search area; A scheduling unit for performing a quick search in the established quadtree model according to this circumscribed rectangle, and performing distance parallel calculations based on the positions of all agricultural machines in the nodes involved in the circumscribed rectangle, so as to find the K nearest agricultural machines for scheduling to complete the rush harvest task.

7. An electronic device, characterized in that, Including: A memory storing computer-executable instructions; A processor configured to run the computer-executable instructions, wherein, when the computer-executable instructions are run by the processor, the steps of the agricultural machinery scheduling method based on interval search according to any one of claims 1-5 are implemented.

8. A storage medium, characterized in that, A computer program is stored on the storage medium, and when the computer program is executed by a processor, it is used to implement the steps of the agricultural machinery scheduling method based on interval search according to any one of claims 1-5.

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