Digital farmland brain-based agricultural machine and unmanned aerial vehicle cooperative operation scheduling method

Abstract

The application relates to a digital farmland brain-based agricultural machine and unmanned aerial vehicle cooperative operation scheduling method, and belongs to the field of data processing. In the method, the digital farmland task splitting decision is sunk from the cloud to the base station side, and the real-time sensing capability of the base station for the signal coverage area is utilized, so that the task allocation and the wireless environment are deeply coupled for the first time. Specifically, after the base station obtains the digital farmland task from the cloud, instead of directly forwarding, the specified farmland area is dynamically split into a first sub-area and a second sub-area suitable for unmanned aerial vehicle and agricultural machine equipment execution based on the signal quality characteristics (such as RSRP, RSRQ, SINR, etc.) of the original sensing, the technical problem that the equipment may enter the signal blind area for operation and cause control failure in the existing cloud scheduling scheme is solved, the initial allocation of the operation task is naturally adapted to the wireless environment, and the reliability and resource utilization efficiency of the cooperative operation are improved from the source.

Description

Technical Field

[0001] This invention belongs to the field of data processing and relates to a method for scheduling collaborative operations of agricultural machinery and drones based on a digital farmland brain. Background Technology

[0002] Currently, in the field of smart agriculture, the collaborative operation scheduling of agricultural machinery and drones typically adopts a centralized cloud control solution. Specifically, the cloud platform obtains information such as the geographical boundaries of the designated farmland area and the type of operation (such as sowing and spraying) based on the prescription map generated by the digital farmland brain. It then generates the optimal operation path for each agricultural machine or drone through path planning algorithms and distributes the operation path to the agricultural machine or drone for execution to achieve automated operation.

[0003] However, this method is not very reliable in actual operation. Summary of the Invention

[0004] In view of this, to solve the above problems, the present invention provides a method for scheduling collaborative operations of agricultural machinery and drones based on a digital farmland brain. To achieve the above objective, the present invention provides the following technical solution:

[0005] In a first aspect, a method for scheduling collaborative operations between agricultural machinery and unmanned aerial vehicles (UAVs) based on a digital farmland brain is provided. The method is characterized by being applied to a base station and includes the following steps: the base station obtains a digital farmland task from a cloud center. The digital farmland task indicates that sowing and / or spraying tasks need to be performed on a designated farmland area, and the designated farmland area is covered by the base station's signal. Based on the signal quality characteristics of the designated farmland area, the base station divides the digital farmland task into a first type of sub-task for a first sub-area and a second type of sub-task for a second sub-area. The first and second sub-areas are combined to form the designated farmland area. The first type of sub-task is applicable to UAVs, and the second type of sub-task is applicable to agricultural machinery. Both the first and second types of sub-tasks include sowing and / or spraying tasks. The base station triggers the corresponding UAV to execute the first type of sub-task and triggers the corresponding agricultural machinery to execute the second type of sub-task.

[0006] Therefore, this method, by shifting the decision-making process for splitting digital farmland tasks from the cloud to the base station, leverages the base station's real-time perception capability of signal coverage areas to achieve, for the first time, deep coupling between task allocation and the wireless environment. Specifically, after obtaining digital farmland tasks from the cloud, the base station does not directly forward them. Instead, based on its natively perceived signal quality characteristics (such as RSRP, RSRQ, SINR, etc.), it dynamically splits the designated farmland area into a first sub-region and a second sub-region, respectively suitable for execution by UAVs and agricultural machinery. This solves the technical problem in existing cloud scheduling schemes where equipment may enter signal blind spots, leading to control failures. It makes the initial allocation of tasks naturally adaptable to the wireless environment, fundamentally improving the reliability and resource utilization efficiency of collaborative operations.

[0007] Optionally, the base station, based on the signal quality characteristics of the designated farmland area, divides the digital farmland task into a first type of sub-task for a first sub-region and a second type of sub-task for a second sub-region. This includes: the base station rasterizes the designated farmland area to obtain K grids, where K is an integer greater than 3; the base station determines the area formed by the grids in the K grids whose signal quality characteristics are better than a preset threshold as the first sub-region, and determines the area formed by the grids in the K grids whose signal quality characteristics are not better than the preset threshold as the second sub-region, thereby dividing the digital farmland task into a first type of sub-task for the first sub-region and a second type of sub-task for the second sub-region.

[0008] Therefore, by specifying farmland areas through rasterization and dividing the area based on signal quality characteristics, the continuous geographic space is discretized into independently assessable units, realizing the spatial mapping between signal quality characteristics and the work area. The signal quality of each raster is independently assessed, making the task splitting granularity controllable and the computational complexity moderate. Compared to dividing by the entire field, the rasterization method can identify differences in signal quality within the same field (such as edges versus centers, occlusion versus openness), avoiding the incorrect allocation of the entire area due to local signal problems, and improving the accuracy and rationality of task splitting.

[0009] Optionally, the base station defines the region consisting of grids with signal quality characteristics better than a preset threshold from the K grids as a first sub-region, and the region consisting of grids with signal quality characteristics not better than the preset threshold from the K grids as a second sub-region, including: for the i-th grid among the K grids: the base station obtains the estimated signal quality characteristics of the i-th grid, the signal quality characteristics including at least one of reference signal received power (RSRP), reference signal received quality (RSRQ), and signal-to-interference-plus-noise ratio (SINR); if the estimated signal quality characteristics of the i-th grid are better than the preset threshold, the base station assigns the i-th grid to the first sub-region; if the estimated signal quality characteristics of the i-th grid are not better than the preset threshold, the base station assigns the i-th grid to the second sub-region.

[0010] Therefore, the above scheme achieves precise matching between "good signal → first sub-region (UAV)" and "poor signal → second sub-region (agricultural machinery)". RSRP reflects coverage strength, RSRQ reflects overall quality, and SINR reflects interference level. The complementary use of these three metrics can comprehensively characterize the wireless environment. This method applies fundamental measurement indicators from the field of communication to the spatial division of agricultural operations for the first time, enabling UAVs to prioritize areas with high signal quality to ensure high-definition image transmission, while agricultural machinery takes over areas with poor signal quality to leverage its anti-interference advantages.

[0011] Optionally, if the signal quality characteristics estimated for the i-th grid are not better than a preset threshold, the method further includes: the base station determining whether the signal quality characteristics estimated for other grids are all better than the preset threshold, wherein the other grids are grids adjacent to the i-th grid; if the signal quality characteristics estimated for other grids are all better than the preset threshold, the base station still assigns the i-th grid to the first sub-region.

[0012] Therefore, by introducing a consistency judgment between adjacent grids to correct isolated signal-degraded grids, fragmentation of the task area caused by measurement deviations of individual grids is avoided. In actual farmland environments, signal quality may exhibit local anomalies due to transient interference or measurement errors. Strictly dividing the area according to thresholds would result in isolated "holes" in the first sub-region, increasing the complexity of task scheduling. This method, through joint judgment of adjacent grids, assigns abnormal grids to surrounding high-quality areas, ensuring the continuity and integrity of sub-regions, reducing the complexity of task path planning, and decreasing the frequency of equipment switching across regions.

[0013] Optionally, the base station triggers the corresponding drone to perform a first type of sub-task and triggers the corresponding agricultural machinery to perform a second type of sub-task, including: the base station determines, based on the relevant information of the drone, a target drone suitable for performing the first type of sub-task and a candidate drone suitable for performing the first type of sub-task; the base station determines, based on the relevant information of the agricultural machinery, a target agricultural machinery suitable for performing the second type of sub-task and a candidate agricultural machinery suitable for performing the second type of sub-task; the base station triggers the target drone to perform the first type of sub-task and triggers the target agricultural machinery to perform the second type of sub-task.

[0014] Therefore, by separately identifying target equipment and candidate equipment, a dual-path execution mechanism with primary and backup was established. The target equipment is responsible for executing the assigned sub-tasks, while the candidate equipment serves as a hot backup, ready to quickly take over in case of primary equipment failure, low power, or signal degradation. This mechanism complements the signal quality-driven regional division: the first sub-region (high-quality signal area) is configured with UAVs as primary and backup, while the second sub-region (general signal area) is configured with agricultural machinery as primary and backup. This ensures operational continuity while avoiding resource waste caused by deploying agricultural machinery in high-quality signal areas. Equipment selection and signal quality characteristics form a closed-loop optimization.

[0015] Optionally, the base station triggers the target drone to execute a first type of sub-task and triggers the target agricultural machinery to execute a second type of sub-task, including: the base station determines that the first type of sub-task includes M sub-tasks based on the grids contained in the first sub-region, where M is an integer greater than 1. In the M sub-tasks, the 1st to the (M-1)th sub-tasks each contain L1 grids in consecutive positions, where L1 is an integer greater than 1. The Mth sub-task contains L1 grids in consecutive positions. The target drones are M target drones, and the pth target drone among the M target drones is used to execute the pth sub-task among the M sub-tasks, where p is an integer from 1 to M. For the pth sub-task: the base station obtains relevant information about the pth target drone from the cloud, and plans a path p for the area contained in the pth sub-task. The base station triggers the pth target drone to execute the pth sub-task along the path p.

[0016] Furthermore, based on the grids contained in the second sub-region, the base station determines that the second type of subtask includes N subtasks, where N is an integer greater than 1. In the N subtasks, the 1st to the (N-1th)th subtasks each contain L2 grids that are consecutive in position, where L2 is an integer greater than 1. The Nth subtask contains L2 grids that are consecutive in position. There are N target agricultural machinery devices. The qth target agricultural machinery device among the N target agricultural machinery devices is used to execute the qth subtask among the N subtasks, where q is an integer from 1 to N. For the qth subtask: the base station obtains relevant information about the qth target agricultural machinery device from the cloud, which is the path q planned for the area contained in the qth subtask. The base station triggers the qth target agricultural machinery device to execute the qth subtask along the path q.

[0017] Therefore, by aggregating continuous grids into subtasks and planning independent paths for each subtask, reasonable task granularity and path optimization are achieved. Each subtask contains L1 or L2 continuous grids, ensuring sufficient working area for each subtask (reducing equipment switching frequency) while avoiding scheduling rigidity caused by excessively large subtasks. Path planning is executed by the cloud based on the characteristics of the target equipment, with the base station responsible for triggering execution, forming a hierarchical architecture of macro-planning in the cloud and micro-scheduling in the base station. Subtasks can be executed in parallel, allowing M target drones and N target agricultural machines to operate simultaneously, significantly improving overall operational efficiency.

[0018] Optionally, during the process of the p-th target UAV executing the p-th sub-task along path p, the method further includes: the base station acquiring the overall signal quality characteristics of one or more grids corresponding to the p-th sub-task in real time; the one or more grids are the grids where the p-th target UAV has not yet executed the p-th sub-task; if the overall signal quality characteristics of one or more grids change to a level not better than a preset threshold, the base station triggers the candidate agricultural machinery to execute the p-th sub-task for one or more grids, and instructs the p-th target UAV to stop executing the p-th sub-task.

[0019] Therefore, by monitoring the overall signal quality characteristics of unexecuted grids in real time and triggering agricultural machinery to take over drone tasks when signal quality deteriorates, true two-way dynamic switching is achieved. This mechanism solves a core pain point in drone operations: when a drone flies into a signal-degraded area, continued execution may lead to image transmission interruption or control loss. This method anticipates risks at the onset of signal quality changes and proactively transfers subsequent tasks to agricultural machinery with strong anti-interference capabilities, allowing the agricultural machinery to continue sowing or spraying operations. The switching process does not rely on the cloud; it is completed at the base station level within milliseconds, ensuring operational continuity and safety.

[0020] Optionally, the base station triggers the candidate agricultural machinery to perform the p-th sub-task for one or more grids, including: the base station obtains relevant information about the candidate agricultural machinery from the cloud based on the cloud, plans a sub-path p for the area contained in one or more grids, and the base station triggers the candidate agricultural machinery to perform the p-th sub-task along the sub-path p.

[0021] Therefore, by replanning sub-paths for the switched task, it is ensured that the replacement equipment can efficiently execute the transferred task. The operating parameters (speed, turning radius, working width) of candidate agricultural machinery differ from those of drones, meaning the original path cannot be directly reused. The base station triggers the cloud to replan the sub-path based on the characteristics of the candidate agricultural machinery. The path precisely matches the remaining area of ​​the sub-task, avoiding efficiency losses caused by the agricultural machinery following "drone paths." This mechanism ensures that operational efficiency does not decrease after task switching, and that the sub-path planning seamlessly connects with the original path, avoiding duplicate operations or omissions.

[0022] Optionally, during the process of the qth target agricultural machinery device executing the qth sub-task along path q, the method further includes: the base station acquiring the overall signal quality characteristics of at least one grid corresponding to the qth sub-task in real time; the at least one grid is a grid in which the qth target agricultural machinery device has not yet executed the qth sub-task; if the overall signal quality characteristics of at least one grid change to a level better than a preset threshold, the base station triggers the candidate UAV to execute the qth sub-task for at least one grid and instructs the qth target agricultural machinery device to stop executing the qth sub-task.

[0023] Therefore, when signal quality improves in areas with poor signal due to interference elimination or equipment relocation, this method can automatically switch subsequent tasks from agricultural machinery to drones. Drones are generally more efficient than agricultural machinery (higher flight speed, not limited by terrain), and switching to drones after signal improvement can increase the efficiency of completing remaining tasks. This mechanism, symmetrical to the aforementioned switching direction, together form a complete two-way dynamic scheduling closed loop.

[0024] Optionally, the base station triggers the candidate drone to perform the q-th subtask for at least one grid, including: the base station obtains relevant information about the candidate drone from the cloud, plans a sub-path q for the area contained in at least one grid, and the base station triggers the candidate drone to perform the q-th subtask along the sub-path q.

[0025] Therefore, by replanning sub-paths for drones to take over tasks, it is possible to ensure that drones can efficiently perform tasks transferred from agricultural machinery. The operational characteristics (flight altitude, speed, flight path pattern) of candidate drones differ from those of agricultural machinery, necessitating the replanning of sub-paths adapted to these characteristics. Cloud-based replanning based on candidate drone parameters fully leverages the drones' high-efficiency operational capabilities, potentially increasing operational efficiency several times over. This mechanism ensures that efficient equipment can be activated immediately after signal improvement, achieving dynamic optimization of operational efficiency.

[0026] The objectives and other advantages of this invention can be realized and obtained through the following description. Attached Figure Description

[0027] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0028] Figure 1 A schematic diagram of the architecture of a collaborative work system provided by the present invention;

[0029] Figure 2 A flowchart of a collaborative operation scheduling method for agricultural machinery and drones based on a digital farmland brain provided by the present invention;

[0030] Figure 3 This is a schematic diagram of the structure of a processing device provided by the present invention. Detailed Implementation

[0031] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. The accompanying drawings are for illustrative purposes only, representing only schematic diagrams and not actual physical objects, and should not be construed as limiting the present invention. To better illustrate the embodiments of the present invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product form.

[0032] like Figure 1 As shown in the figure, this application provides a collaborative operation system, which mainly includes a base station, a drone, and agricultural machinery.

[0033] The base station adopts an Open Radio Access Network (O-RAN) architecture, including an Open Distributed Unit (O-DU) and an Open Centralized Unit (O-CU). The O-DU is deployed in the site equipment room or at the edge node, implementing Physical Layer (PHY) and Medium Access Control (MAC) functions, and is responsible for real-time measurement of signal quality characteristics at various locations within its coverage area. These signal quality characteristics include at least one of Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal-to-Interference-plus-Noise Ratio (SINR). The O-CU implements Radio Resource Control (RRC) and Packet Data Convergence Protocol (PDCP) layer functions, and carries the decision logic of the cooperative job scheduling method provided in this application embodiment. The base station is connected to the cloud center to receive digital farmland tasks issued by the cloud center. The digital farmland tasks indicate the types of operations that need to be performed on a specified farmland area. The types of operations include sowing tasks and / or spraying tasks.

[0034] The drone is an unmanned aerial vehicle capable of performing seeding and pesticide application tasks. It includes an airframe, power system, seeding system, flight control system, positioning module, and 5G communication module. The seeding system includes a seeding disc or centrifugal spraying disc for uniform seed distribution or precise pesticide application. The positioning module supports Global Positioning System (GPS) or Real-Time Kinematic (RTK) positioning to obtain the drone's real-time location. The 5G communication module establishes a 5G air interface connection with a base station, reporting the drone's real-time location, speed, remaining battery power, and operational status, and receiving task queues and path instructions from the base station. The flight control system autonomously controls the drone to fly along a planned path and perform operations according to the path instructions from the base station.

[0035] The agricultural machinery is an autonomous vehicle capable of performing sowing and spraying tasks. It includes a vehicle body, power system, sowing and fertilizing device, spraying device, automatic driving system, positioning module, and 5G communication module. The sowing and fertilizing device enables simultaneous sowing and fertilization or variable-rate fertilization, while the spraying device enables precision spraying. The automatic driving system supports satellite navigation-based path tracking control. The positioning module supports GPS or RTK positioning to obtain the real-time location of the agricultural machinery. The 5G communication module establishes a 5G air interface connection with a base station, reporting the real-time location, speed, remaining fuel or battery power, and operational status information of the agricultural machinery, and receiving task queues and path instructions from the base station. The automatic driving system autonomously controls the agricultural machinery to travel along the planned path and perform operations according to the path instructions issued by the base station.

[0036] In this embodiment, both the drone and the agricultural machinery are capable of performing sowing and spraying tasks, meaning they are functionally interchangeable. The base station, acting as the scheduling decision core, uses its O-CU to divide the digital farmland task into a first type of sub-task for a first sub-region and a second type of sub-task for a second sub-region based on the signal quality characteristics of the designated farmland area measured by the O-DU. The first type of sub-task is suitable for drone execution, and the second type of sub-task is suitable for agricultural machinery execution. Both types of sub-tasks include sowing and / or spraying tasks. The O-CU is also used to trigger the corresponding drone to execute the first type of sub-task and the corresponding agricultural machinery to execute the second type of sub-task via the O-DU.

[0037] like Figure 2 As shown in the figure, this application provides a method for scheduling collaborative operations of agricultural machinery and drones based on a digital farmland brain. This method is applied to the above-mentioned system, and the specific process of the method is as follows:

[0038] S201, the base station obtains digital farmland tasks from the cloud center.

[0039] The digital farmland task instruction requires the execution of sowing and / or spraying tasks in a designated farmland area, which is covered by the base station's signal.

[0040] The base station's O-CU establishes a communication connection with the cloud center through the core network and obtains digital farmland tasks from the cloud center. The digital farmland task indicates that sowing and / or spraying tasks need to be performed on a designated farmland area, which is covered by the signal coverage range of the base station's O-DU.

[0041] A digital farmland task includes at least the geographic boundary coordinates of the specified farmland area, a task type identifier, a task quantity requirement, and a farming time window. The geographic boundary coordinates are represented as a list of the latitude and longitude coordinates of polygon vertices; for example, the boundary of a rectangular field can be represented by the coordinates of its four vertices. The task type identifier indicates whether a sowing task and / or a pesticide application task are to be performed. For example, "01" indicates sowing only, "02" indicates pesticide application only, and "03" indicates both sowing and pesticide application. The task quantity requirement includes the sowing amount or pesticide application amount. For variable task scenarios, the task quantity requirement is provided in the form of a prescription map, which is a rasterized map. Each raster is associated with a corresponding task quantity value; for example, each 10m × 10m raster is associated with a sowing amount value, ranging from 100 kg to 200 kg per hectare. The farming time window indicates the allowed execution period of the task, such as "March 1, 2026 to March 10, 2026".

[0042] After acquiring the digital farmland task, the O-CU of the base station stores the task information in its local memory and parses the geographical boundary of the designated farmland area. It then compares this boundary with the base station's signal coverage area to confirm that the designated farmland area is entirely within the base station's signal coverage. This ensures that subsequent scheduling instructions can be reliably transmitted to the drones and agricultural machinery performing the operation. If part of the designated farmland area is outside the base station's signal coverage, the O-CU requests supplementary coverage from the cloud center or instructs that operation scheduling be performed only on a portion of the covered area.

[0043] S202, the base station, based on the signal quality characteristics of the designated farmland area, divides the digital farmland task into a first type of sub-task for the first sub-area and a second type of sub-task for the second sub-area.

[0044] The designated farmland area is formed by combining the first and second sub-regions. The first type of sub-task is applicable to drones, while the second type of sub-task is applicable to agricultural machinery. Both types of sub-tasks include sowing and / or spraying tasks.

[0045] Specifically, the base station rasterizes the designated farmland area, resulting in K grids. Rasterization can be based on the prescription map in the digital farmland task; the prescription map itself is a rasterized map, and each grid is associated with a corresponding workload value. For example, if the designated farmland area is a 600m x 600m square plot, the base station divides this plot into 100m x 100m grids, resulting in 6 rows and 6 columns, totaling 36 grids. Each grid corresponds to a 100m x 100m work unit. The value of K can be determined according to actual accuracy requirements; in this embodiment, K is an integer greater than 3.

[0046] The base station defines the area formed by the grids in the K grids whose signal quality characteristics are better than a preset threshold as the first sub-region, and defines the area formed by the grids in the K grids whose signal quality characteristics are not better than the preset threshold as the second sub-region. Thus, the digital farmland task is divided into a first type of sub-task for the first sub-region and a second type of sub-task for the second sub-region.

[0047] Specifically, the base station rasterizes the designated farmland area, resulting in K grids. Rasterization can be based on the prescription map in the digital farmland task; the prescription map itself is a rasterized map, and each grid is associated with a corresponding workload value. For example, if the designated farmland area is a 60m x 60m square plot, the base station divides this plot into 10m x 10m grids, resulting in 6 rows and 6 columns, totaling 36 grids. Each grid corresponds to a 10m x 10m work unit. The value of K can be determined according to actual accuracy requirements; in this embodiment, K is an integer greater than 3.

[0048] The base station defines the area formed by the grids in the K grids whose signal quality characteristics are better than a preset threshold as the first sub-region, and defines the area formed by the grids in the K grids whose signal quality characteristics are not better than the preset threshold as the second sub-region. Thus, the digital farmland task is divided into a first type of sub-task for the first sub-region and a second type of sub-task for the second sub-region.

[0049] Specifically, for the i-th grid out of K grids, the base station acquires the estimated signal quality characteristics of the i-th grid. These signal quality characteristics include at least one of Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal-to-Interference-plus-Noise Ratio (SINR). The estimated signal quality characteristics can be calculated by the base station's Open Distributed Unit (O-DU) based on historical measurement data or a signal propagation model. For example, for a given grid, the estimated RSRP value might be -85 dBm, and the SINR value might be 18 dB.

[0050] Base stations preset one or more thresholds to determine signal quality. Taking RSRP as an example, a preset threshold of -95dBm can be set. If the estimated RSRP of the i-th grid is better than -95dBm (i.e., greater than -95dBm, indicating a stronger signal), then the signal quality characteristic of that grid is determined to be better than the preset threshold; if the estimated RSRP of the i-th grid is not better than -95dBm (i.e., less than or equal to -95dBm, indicating a weaker signal), then the signal quality characteristic of that grid is determined to be worse than the preset threshold. When multiple signal quality indicators are used, the base station can set a comprehensive judgment rule, such as requiring RSRP to be better than -95dBm and SINR to be better than 10dB, both of which must be met simultaneously to be considered as better than the preset threshold.

[0051] If the signal quality characteristics estimated for the i-th grid are better than a preset threshold, the base station assigns the grid to the first sub-region; if the signal quality characteristics estimated for the i-th grid are not better than the preset threshold, the base station assigns the grid to the second sub-region.

[0052] For example, suppose a designated farmland area is divided into 36 grids of 6×6 grids, each grid measuring 10 meters × 10 meters, and numbered from (1,1) to (6,6). The base station obtains the estimated RSRP value for each grid through the O-DU and sets a preset threshold of -95 dBm. The measurement results are as follows:

[0053] Row 1: The RSRP of grids (1,1) to (1,6) are -90dBm, -88dBm, -86dBm, -87dBm, -89dBm and -92dBm respectively, all of which are better than -95dBm.

[0054] Row 2: The RSRP of grids (2,1) to (2,6) are -85dBm, -83dBm, -84dBm, -86dBm, -82dBm and -91dBm respectively, all of which are better than -95dBm.

[0055] Row 3: The RSRP of grids (3,1) to (3,6) are -80dBm, -96dBm, -97dBm, -98dBm, -96dBm, and -79dBm, respectively. Among them, the RSRP of grids (3,2), (3,3), (3,4), and (3,5) are -96dBm, -97dBm, -98dBm, and -96dBm, respectively, none of which are better than -95dBm.

[0056] Row 4: The RSRP of grids (4,1) to (4,6) are -78dBm, -97dBm, -98dBm, -99dBm, -97dBm, and -77dBm, respectively. Among them, the RSRP of grids (4,2), (4,3), (4,4), and (4,5) are -97dBm, -98dBm, -99dBm, and -97dBm, respectively, none of which are better than -95dBm.

[0057] Row 5: The RSRP of grids (5,1) to (5,6) are -76dBm, -77dBm, -78dBm, -79dBm, -80dBm and -75dBm respectively, all of which are better than -95dBm.

[0058] Row 6: The RSRP of grids (6,1) to (6,6) are -74dBm, -75dBm, -94dBm, -76dBm, -77dBm, and -73dBm, respectively. Among them, the RSRP of grid (6,3) is -94dBm, which is better than -95dBm.

[0059] Based on the above judgment, eight grids (3,2), (3,3), (3,4), (3,5), (4,2), (4,3), (4,4), and (4,5) are not better than the preset threshold, and the base station divides these eight grids into the second sub-region; the remaining 28 grids are all better than the preset threshold, and the base station divides them into the first sub-region.

[0060] Optionally, if the estimated signal quality characteristics of the i-th grid are not better than a preset threshold, the base station also performs adjacent grid consistency correction. Specifically, the base station determines whether the estimated signal quality characteristics of other grids are all better than the preset threshold. These other grids are the grids adjacent to the i-th grid (including eight neighborhoods in the top, bottom, left, right, and diagonal directions; the number of neighborhoods for grids located at the boundaries is reduced accordingly). If the estimated signal quality characteristics of all adjacent grids are better than the preset threshold, the base station still assigns the i-th grid to the first sub-region, not the second sub-region. This correction mechanism is used to avoid fragmentation of the task area due to measurement deviations or transient interference in a single grid, ensuring the continuity and integrity of the sub-regions.

[0061] Continuing the previous example, the RSRP of grid (3,2) is -96dBm, which is not better than -95dBm. Its adjacent grids include (2,1), (2,2), (2,3), (3,1), (3,3), (4,1), (4,2), and (4,3). Among them, (3,3) and (4,2) and (4,3) are not better than the preset threshold. Therefore, there are adjacent grids that are not better than the preset threshold, which does not meet the condition that "all adjacent grids are better than the threshold". Therefore, grid (3,2) is still assigned to the second sub-region.

[0062] S203, the base station triggers the corresponding drone to execute the first type of sub-task, and triggers the corresponding agricultural machinery to execute the second type of sub-task.

[0063] Based on the drone's relevant information, the base station identifies the target drone suitable for performing the first type of sub-task, as well as candidate drones suitable for performing the first type of sub-task. The drone's relevant information includes its real-time location, remaining battery power, current operating status, communication requirement level, and idle status. The base station selects from the currently connected drone list a drone located near the first sub-area, with sufficient remaining battery power, currently idle, and capable of seeding and / or spraying as the target drone. Simultaneously, the base station selects one or more additional drones as candidate drones to quickly take over the task if the primary drone malfunctions, runs out of battery power, or experiences signal quality degradation. Similarly, based on the agricultural machinery's relevant information, the base station identifies the target agricultural machinery suitable for performing the second type of sub-task, as well as candidate agricultural machinery suitable for performing the second type of sub-task. The agricultural machinery's relevant information includes its real-time location, remaining fuel or battery power, current operating status, and idle status. The base station selects agricultural machinery that is located close to the second sub-region, has sufficient remaining energy, is in an idle state, and has the ability to perform sowing and / or spraying from the list of currently accessed agricultural machinery equipment as the target agricultural machinery equipment, and additionally selects one or more agricultural machinery equipment as candidate agricultural machinery equipment.

[0064] Therefore, the base station triggers the target drone to execute the first type of sub-task and triggers the target agricultural machinery to execute the second type of sub-task.

[0065] Specifically, the base station determines that the first type of subtask includes M subtasks based on the grids contained in the first sub-region, where M is an integer greater than 1. In the M subtasks, the 1st to the (M-1th)th subtasks each contain L1 consecutive grids, where L1 is an integer greater than 1. The Mth subtask contains L1 consecutive grids. For example, in the 6×6 grid example above, the first sub-region contains 28 grids, distributed as follows: all 6 grids in the first row (1,1) to (1,6); all 6 grids in the second row (2,1) to (2,6); (3,1) and (3,6) in the third row; (4,1) and (4,6) in the fourth row; all 6 grids in the fifth row (5,1) to (5,6); and all 6 grids in the sixth row (6,1) to (6,6). The base station aggregates consecutive grid cells into subtasks, each subtask containing three consecutive grid cells. For example, the first three grid cells of the first row (1,1), (1,2), and (1,3) are aggregated into subtask 1; the last three grid cells of the first row (1,4), (1,5), and (1,6) are aggregated into subtask 2; the first three grid cells of the second row (2,1), (2,2), and (2,3) are aggregated into subtask 3; the last three grid cells of the second row (2,4), (2,5), and (2,6) are aggregated into subtask 4; (3,1) of the third row, (4,1) of the fourth row, and (5,1) of the fifth row are aggregated into subtask 5, these three grid cells being consecutive in the vertical direction; (5,2), (5,3), and (5,4) of the fifth row are aggregated into subtask 6; and so on. The (5,5) and (5,6) of row 5 are aggregated with (6,6) of row 6 to form subtask 7. However, (5,6) and (6,6) are consecutive, while (5,5) and (5,6) are consecutive, thus forming three consecutive grids. The (6,1), (6,2), and (6,3) of row 6 are aggregated to form subtask 8. The (6,4) and (6,5) of row 6 are aggregated with (3,6) of row 3 to form subtask 9. Here, (6,5) and (6,4) are consecutive, while (3,6) and (6,6), although not directly adjacent, can be connected through (5,6), allowing for flexible combination during actual aggregation. The remaining grid (4,6) is treated as subtask 10, containing one grid. Thus, M is 10, L1 is 3, subtasks 1 to 9 each contain 3 consecutive grids, and subtask 10 contains 1 grid. For isolated grids within the first sub-region, if there are a large number of isolated grids, the base station can disregard the location continuity condition and determine the region where each grid is located as the region corresponding to a sub-task, that is, each sub-task contains only 1 grid.

[0066] Furthermore, there are M target drones. The p-th target drone among the M target drones is used to execute the p-th subtask among the M subtasks, where p is an integer from 1 to M. When allocating subtasks, the base station can optimize the allocation based on the real-time location and remaining battery power of each drone. For example, it can assign subtasks that are close to each other to the same drone, or assign multiple subtasks to the same drone to make full use of its endurance. In this case, the number of target drones can be less than M. For the p-th subtask, the base station obtains relevant information about the p-th target drone from the cloud and plans the path p for the area included in the p-th subtask. When planning the path, the cloud considers the target drone's operating width, maximum speed, endurance, and the boundary shape of the subtask area to generate the optimal operating path. For example, for a subtask consisting of multiple consecutive grids, the cloud generates a sweeping flight path covering the area. This can be existing technology, and this application embodiment does not limit it. For a subtask consisting of a single grid, the cloud generates a simple flight path covering the grid area. This can be existing technology, and this application embodiment does not limit it. After the base station obtains path p, it triggers the p-th target UAV to execute the p-th subtask along path p through the Open Distributed Unit (O-DU) and Open Radio Unit (O-RU).

[0067] Similarly, based on the grids contained in the second sub-region, the base station determines that the second type of subtask comprises N subtasks, where N is an integer greater than 1. In the N subtasks, the 1st to the (N-1th)th subtasks each contain L2 consecutive grids, where L2 is an integer greater than 1. The Nth subtask contains L2 consecutive grids. For example, in the 6×6 grid example above, the second sub-region contains 8 grids: (3,2), (3,3), (3,4), (3,5), (4,2), (4,3), (4,4), and (4,5). These grids form a rectangular area of ​​2 rows and 4 columns. The base station aggregates consecutively distributed grids within this area into subtasks, each subtask containing 3 consecutive grids. For example, (3,2), (3,3), and (3,4) are aggregated into subtask 1; (3,5), (4,5), and (4,4) are aggregated into subtask 2; and (4,2) and (4,3) are aggregated into subtask 3, which contains 2 grids. Thus, N is 3, L2 is 3, the first and second subtasks each contain 3 consecutive grids, and the third subtask contains 2 consecutive grids. For isolated grids within the second sub-region, if the number of isolated grids is large, the base station can disregard the location continuity condition and determine the region corresponding to each grid as the region of a subtask.

[0068] Furthermore, there are N target agricultural machinery devices. The q-th target agricultural machinery device among the N target agricultural machinery devices is used to execute the q-th sub-task among the N sub-tasks, where q is an integer from 1 to N. When allocating sub-tasks, the base station can optimize the allocation based on the real-time location and remaining energy of each agricultural machinery device. For example, it can assign sub-tasks that are close to each other to the same agricultural machinery device, or assign multiple sub-tasks to the same agricultural machinery device to fully utilize its operating capacity. In this case, the number of target agricultural machinery devices can be less than N. For the q-th sub-task, the base station obtains relevant information about the q-th target agricultural machinery device from the cloud and plans the path q for the area included in the q-th sub-task. When planning the path, the cloud considers the operating width, turning radius, maximum speed of the target agricultural machinery device, and the boundary shape of the sub-task area to generate the optimal operating path. For example, for a sub-task consisting of multiple consecutive grids, the cloud generates a reciprocating operating path covering the area, including the turning point at the edge of the field. Specifically, it can be existing technology, and this application embodiment does not limit it. After the base station obtains the path q, it triggers the q-th target agricultural machinery to execute the q-th sub-task along the path q through O-DU and O-RU.

[0069] During execution, the drones and agricultural machinery continuously report real-time location, work progress, remaining battery power or fuel, and other information to the base station via the 5G communication module. The base station monitors the task execution status based on the reported information. When the base station detects that the target drone or agricultural machinery has malfunctioned, has insufficient power, or that the signal quality in the area has changed significantly, the base station can trigger a candidate drone or agricultural machinery to take over the unfinished task. The specific switching process will be described in detail in subsequent embodiments.

[0070] Optionally, during the process of the p-th target UAV performing the p-th subtask along path p, the method further includes:

[0071] The base station acquires the overall signal quality characteristics of one or more grids corresponding to the p-th subtask in real time; these grids are those where the p-th target UAV has not yet executed the p-th subtask. Specifically, the base station continuously measures the signal quality characteristics of the grids included in the p-th subtask using an Open Distributed Unit (O-DU), including at least one of Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal-to-Interference-plus-Noise Ratio (SINR). The base station treats the grids in the unexecuted portion of the subtask as a whole and calculates their average RSRP value or comprehensive signal quality score as the overall signal quality characteristic.

[0072] If the overall signal quality characteristics of one or more grids change to a level not better than a preset threshold, the base station triggers the candidate agricultural machinery to execute the p-th sub-task for one or more grids and instructs the p-th target drone to stop executing the p-th sub-task. For example, continuing with the aforementioned 6×6 grid example, suppose the p-th sub-task is sub-task 6 in the first sub-region, which includes grids (5,2), (5,3), and (5,4). The drone is currently executing the operation on grid (5,2), and the remaining unexecuted grids are (5,3) and (5,4). The base station's O-DU monitors in real time that the RSRP values ​​of grids (5,3) and (5,4) drop from -78dBm and -79dBm to -98dBm and -99dBm, respectively, both below the preset threshold of -95dBm, and remain below this threshold for more than 3 seconds. The base station determines that the signal quality of the remaining area of ​​this sub-task no longer meets the requirements for drone operation. The base station selects an idle agricultural machinery device from the pool of candidate agricultural machinery devices that is close to the sub-task. For example, candidate agricultural machinery device B is currently located near grid (4,1) and has sufficient remaining fuel. The base station requests the cloud to plan a sub-path p for the area contained in grids (5,3) and (5,4). The cloud generates an operation path covering these two grids based on parameters such as the working width and turning radius of candidate agricultural machinery device B. After obtaining the sub-path p, the base station issues a stop command to the p-th target UAV, instructing it to end its current operation and return to base or hover for further orders. At the same time, it issues a task takeover command to candidate agricultural machinery device B, including the sub-path p and the corresponding operation parameters (seeding amount or pesticide application amount). Candidate agricultural machinery device B performs the operation of the remaining grids along the sub-path p, achieving a seamless task switch from UAV to agricultural machinery device.

[0073] Optionally, during the process of the q-th target agricultural machinery equipment performing the q-th subtask along path q, the method further includes:

[0074] The base station acquires the overall signal quality characteristics of at least one grid corresponding to the q-th subtask in real time; at least one grid is the grid where the q-th target agricultural machinery equipment has not yet executed the q-th subtask. Specifically, the base station continuously measures the signal quality characteristics of the grids included in the q-th subtask through O-DU, and calculates the average RSRP value or comprehensive signal quality score of the grids in the unexecuted part of the subtask as a whole.

[0075] If the overall signal quality characteristics of at least one grid change to a level better than a preset threshold, the base station triggers the candidate drone to execute the q-th sub-task for at least one grid and instructs the q-th target agricultural machinery to stop executing the q-th sub-task. For example, continuing with the aforementioned 6×6 grid example, suppose the q-th sub-task is sub-task 1 in the second sub-region, which includes grids (3,2), (3,3), and (3,4). The agricultural machinery is currently executing the operation on grid (3,2), and the remaining unexecuted grids are (3,3) and (3,4). The base station's O-DU monitors in real time that the RSRP values ​​of grids (3,3) and (3,4) have increased from -97dBm and -98dBm to -88dBm and -89dBm, respectively, both better than the preset threshold of -95dBm, and have remained above this threshold for more than 5 seconds. The base station determines that the signal quality of the remaining area of ​​this sub-task meets the requirements for drone operation, and that using a drone can improve operational efficiency. The base station selects an idle drone from the candidate drone pool that is close to the sub-task. For example, candidate drone C is currently near grid (2,5) and has sufficient remaining power. The base station requests a sub-path q from the cloud for the area contained in grids (3,3) and (3,4). The cloud generates a sweeping flight path covering these two grids based on parameters such as the working width and flight speed of candidate drone C. After obtaining the sub-path q, the base station issues a stop command to the q-th target agricultural machinery, instructing it to end its current operation and wait in place or leave. At the same time, it issues a task takeover command to candidate drone C, including the sub-path q and the corresponding operation parameters. Candidate drone C performs the operation of the remaining grids along the sub-path q, achieving a seamless task switch from agricultural machinery to drone.

[0076] Through the aforementioned two-way dynamic switching mechanism, the base station can assign tasks to more suitable equipment types in real time when signal quality changes. This ensures that the drone always operates in areas with high-quality signals to guarantee the reliability of high-definition image transmission and control, while also leveraging the anti-interference advantages of agricultural machinery in areas with poor signals. At the same time, it can switch back to the high-efficiency drone in a timely manner when the signal improves, thus achieving coordinated optimization of communication resources and operational resources.

[0077] In summary, this method, by shifting the decision-making process for splitting digital farmland tasks from the cloud to the base station, leverages the base station's real-time perception capability of signal coverage areas to achieve, for the first time, deep coupling between task allocation and the wireless environment. Specifically, after obtaining digital farmland tasks from the cloud, the base station does not directly forward them. Instead, based on its natively perceived signal quality characteristics (such as RSRP, RSRQ, SINR, etc.), it dynamically splits the designated farmland area into a first sub-region and a second sub-region, respectively suitable for execution by UAVs and agricultural machinery. This solves the technical problem in existing cloud-based scheduling schemes where equipment may enter signal blind spots, leading to control failures. It ensures that the initial allocation of tasks is naturally adapted to the wireless environment, fundamentally improving the reliability and resource utilization efficiency of collaborative operations.

[0078] Figure 3 This is a schematic diagram of the structure of a processing device provided in an embodiment of this application. Exemplarily, the processing device may be a terminal, or a chip (system) or other component or assembly that can be disposed on the terminal. Figure 3 As shown, the processing device 200 may include a processor 201. Optionally, the processing device 200 may also include a memory 202 and / or a transceiver 203. The processor 201 is coupled to the memory 202 and the transceiver 203, for example, via a communication bus.

[0079] The following is combined Figure 3 A detailed description of each component of the processing equipment 200 is provided below:

[0080] The processor 201 is the control center of the processing device 200. It can be a single processor or a collective term for multiple processing elements. For example, the processor 201 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement the embodiments of this application, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0081] Optionally, the processor 201 can perform various functions of the processing device 200 by running or executing software programs stored in the memory 202 and calling data stored in the memory 202, such as performing the above-mentioned functions. Figure 2 The method shown.

[0082] In a specific implementation, as one example, the processor 201 may include one or more CPUs, for example... Figure 3 CPU0 and CPU1 are shown in the diagram.

[0083] In a specific implementation, as one example, the processing device 200 may also include multiple processors, for example... Figure 3 The processor 201 shown is an example. Each of the processors 201 can be a single-core processor or a multi-core processor. Here, "processor" can refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0084] The memory 202 is used to store the software program that executes the solution of this application, and is controlled by the processor 201 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0085] Optionally, the memory 202 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 202 may be integrated with the processor 201 or exist independently, and may be connected via the interface circuitry of the processing device 200. Figure 3 (Not shown in the image) is coupled to processor 201, but this embodiment does not specifically limit this.

[0086] Transceiver 203 is used for communication with other processing devices. For example, if processing device 200 is a terminal, transceiver 203 can be used to communicate with a network device or with another terminal device. As another example, if processing device 200 is a network device, transceiver 203 can be used to communicate with a terminal or with another network device.

[0087] Optionally, transceiver 203 may include a receiver and a transmitter. Figure 3 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0088] Optionally, the transceiver 203 can be integrated with the processor 201, or it can exist independently and be connected via the interface circuit of the processing device 200. Figure 3 (Not shown in the image) is coupled to processor 201, but this embodiment does not specifically limit this.

[0089] Understandable, Figure 3 The structure of the processing device 200 shown does not constitute a limitation on the processing device. Actual processing devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0090] Furthermore, the technical effects of the processing device 200 can be referred to the technical effects of the method described in the above method embodiments, and will not be repeated here.

[0091] It should be understood that the processor in the embodiments of this application can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0092] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0093] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0094] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0095] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0096] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0097] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0098] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0100] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0101] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0102] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0103] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for scheduling collaborative operations of agricultural machinery and unmanned aerial vehicles based on a digital farmland brain, characterized in that, The method is applied to a base station, and the method includes: The base station obtains digital farmland tasks from the cloud center. The digital farmland tasks indicate that sowing and / or spraying tasks need to be performed on a designated farmland area, and the designated farmland area is covered by the signal of the base station. The base station, based on the signal quality characteristics of the designated farmland area, divides the digital farmland task into a first type of sub-task for a first sub-area and a second type of sub-task for a second sub-area. The first sub-area and the second sub-area are spliced ​​together to form the designated farmland area. The first type of sub-task is applicable to drones, and the second type of sub-task is applicable to agricultural machinery. Both the first type of sub-task and the second type of sub-task include sowing and / or spraying tasks. The base station triggers the corresponding drone to execute the first type of sub-task and triggers the corresponding agricultural machinery to execute the second type of sub-task.

2. The method according to claim 1, characterized in that, Based on the signal quality characteristics of the designated farmland area, the base station divides the digital farmland task into a first type of sub-task for a first sub-region and a second type of sub-task for a second sub-region, including: The base station rasterizes the designated farmland area to obtain K grids, where K is an integer greater than 3; The base station defines the region formed by the grids in the K grids whose signal quality characteristics are better than a preset threshold as the first sub-region, and defines the region formed by the grids in the K grids whose signal quality characteristics are not better than the preset threshold as the second sub-region, thereby splitting the digital farmland task into a first type of sub-task for the first sub-region and a second type of sub-task for the second sub-region.

3. The method according to claim 2, characterized in that, The base station defines the region formed by the grids in the K grids whose signal quality characteristics are better than a preset threshold as the first sub-region, and defines the region formed by the grids in the K grids whose signal quality characteristics are not better than the preset threshold as the second sub-region, including: For the i-th cell among the K cells: The base station obtains the signal quality characteristics estimated for the i-th grid, and the signal quality characteristics include at least one of the following: Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal-to-Interference-Ratio (SINR). If the estimated signal quality characteristics of the i-th grid are better than the preset threshold, the base station will assign the i-th grid to the first sub-region; if the estimated signal quality characteristics of the i-th grid are not better than the preset threshold, the base station will assign the i-th grid to the second sub-region.

4. The method according to claim 3, characterized in that, If the signal quality characteristics estimated for the i-th grid are not better than the preset threshold, the method further includes: The base station determines whether the signal quality characteristics estimated by other grids are all better than the preset threshold, wherein the other grids are the grids adjacent to the i-th grid; If the signal quality characteristics estimated by the other grids are all better than the preset threshold, the base station will still divide the i-th grid into the first sub-region.

5. The method according to any one of claims 2-4, characterized in that, The base station triggers the corresponding drone to execute the first type of sub-task and triggers the corresponding agricultural machinery to execute the second type of sub-task, including: The base station determines, based on relevant information about the drone, the target drone suitable for performing the first type of sub-task, and the candidate drone suitable for performing the first type of sub-task; The base station determines, based on relevant information about the agricultural machinery equipment, the target agricultural machinery equipment suitable for performing the second type of sub-task, and the candidate agricultural machinery equipment suitable for performing the second type of sub-task; The base station triggers the target drone to execute the first type of sub-task and triggers the target agricultural machinery to execute the second type of sub-task.

6. The method according to claim 5, characterized in that, The base station triggers the target drone to execute the first type of sub-task and triggers the target agricultural machinery to execute the second type of sub-task, including: The base station determines, based on the grids contained in the first sub-region, that the first type of subtask includes M subtasks, where M is an integer greater than 1. In the M subtasks, the 1st to the (M-1th)th subtasks each contain L1 consecutive grids, where L1 is an integer greater than 1. The Mth subtask contains L1 consecutive grids. The target UAV consists of M target UAVs, and the pth target UAV among the M target UAVs is used to execute the pth subtask among the M subtasks, where p is an integer from 1 to M. For the p-th subtask: the base station obtains relevant information about the p-th target UAV from the cloud, and plans a path p for the area included in the p-th subtask. The base station then triggers the p-th target UAV to execute the p-th subtask along the path p. The base station determines that the second type of subtask includes N subtasks based on the grids contained in the second sub-region, where N is an integer greater than 1. In the N subtasks, the 1st to the (N-1th)th subtasks each contain L2 consecutive grids, where L2 is an integer greater than 1. The Nth subtask contains L2 consecutive grids. The target agricultural machinery consists of N target agricultural machinery devices, and the qth target agricultural machinery device among the N target agricultural machinery devices is used to execute the qth subtask among the N subtasks, where q is an integer from 1 to N. For the q-th subtask: the base station obtains relevant information about the q-th target agricultural machinery from the cloud, and plans a path q for the area included in the q-th subtask. The base station then triggers the q-th target agricultural machinery to execute the q-th subtask along the path q.

7. The method according to claim 6, characterized in that, During the process of the p-th target UAV performing the p-th sub-task along the path p, the method further includes: The base station acquires the overall signal quality characteristics of one or more grids corresponding to the p-th subtask in real time; the one or more grids are the grids where the p-th target UAV has not yet executed the p-th subtask; If the overall signal quality characteristics of the one or more grids change to a level that is not better than a preset threshold, the base station triggers the candidate agricultural machinery to execute the p-th sub-task for the one or more grids, and instructs the p-th target UAV to stop executing the p-th sub-task.

8. The method according to claim 7, characterized in that, The base station triggers candidate agricultural machinery to execute the p-th sub-task for one or more grids, including: The base station obtains relevant information about the candidate agricultural machinery from the cloud and plans a sub-path p for the area contained in the one or more grids. The base station then triggers the candidate agricultural machinery to execute the p-th sub-task along the sub-path p.

9. The method according to claim 6, characterized in that, During the process of the q-th target agricultural machinery executing the q-th sub-task along the path q, the method further includes: The base station acquires the overall signal quality characteristics of at least one grid corresponding to the q-th subtask in real time; the at least one grid is a grid in which the q-th target agricultural machinery equipment has not yet executed the q-th subtask; If the overall signal quality characteristics of at least one grid change to a level better than a preset threshold, the base station triggers the candidate UAV to execute the q-th sub-task for the at least one grid and instructs the q-th target agricultural machinery to stop executing the q-th sub-task.

10. The method according to claim 9, characterized in that, The base station triggers the candidate drone to perform the q-th subtask for the at least one grid, including: The base station obtains relevant information about the candidate drone from the cloud and plans a sub-path q for the area contained in the at least one grid. The base station then triggers the candidate drone to execute the q-th sub-task along the sub-path q.

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