Unmanned aerial vehicle multi-task load management and control method

Abstract

The invention discloses an unmanned aerial vehicle multi-task load management and control method, relates to the technical field of unmanned aerial vehicle load management and control, and solves the technical problems of low task execution efficiency and insufficient resource utilization rate caused by difficult comprehensive evaluation and optimal scheduling of tasks. The task importance can be comprehensively and accurately evaluated by establishing a task-load mapping library, screening tasks based on load complementation requirements, reducing repeated load carrying and dynamically allocating resources by combining the residual load and the load capacity of the unmanned aerial vehicle. Tasks with small correlation and high priority are preferentially selected for synchronous execution, the utilization rate of load resources is improved, the energy consumption and operation cost of the unmanned aerial vehicle are reduced, a task correlation evaluation mechanism is introduced, resource conflicts and interference in the task execution process are avoided, and the collaboration and efficiency of multi-task execution are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) payload management and control, and in particular to a method for managing and controlling a multi-task UAV payload. Background Art

[0002] With the widespread application of drones in emergency rescue, power inspection, agricultural plant protection and other fields, the demand for multi-task payload management and control of drones is becoming increasingly complex.

[0003] Traditional drone mission management models typically rely on pre-defined task sequences and statically allocated payload resources, lacking the ability to dynamically adjust task priorities and efficiently reuse payload resources. In practical applications, drones often face multiple tasks, task priorities that vary with the environment, and limited payload resources. Existing technologies struggle to comprehensively assess and optimize scheduling based on multi-dimensional factors such as mission urgency, timeliness, importance, and resource consumption. This results in inefficient mission execution, insufficient resource utilization, and an inability to meet mission requirements in complex scenarios. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a method for multi-task payload management and control of unmanned aerial vehicles, which solves the problem of difficulty in comprehensive evaluation and optimal scheduling of tasks, resulting in low task execution efficiency and insufficient resource utilization.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for multi-task payload control of an unmanned aerial vehicle, the method specifically comprising the following steps:

[0006] Priority information is generated based on multi-dimensional priority classification of historical execution tasks, and a task-load mapping library is established in combination with corresponding load requirements;

[0007] Determine the priority of the UAV's tasks to be executed, and select payload complementary tasks based on payload requirements, generating payload complementary and normal analysis signals;

[0008] Process the load complementation analysis signal to determine whether the load complementation tasks are of the same type or have a continuous priority relationship, and generate load secondary analysis and adjustment processing signals;

[0009] Analyze the adjustment processing signal to obtain the highest priority task and load requirement, calculate the remaining load and carrying capacity as comparison criteria, select the tasks to be analyzed that meet the criteria in order of priority, and generate control information;

[0010] Analyze the secondary analysis signal of the load and generate control information based on the relationship between the total load and total carrying capacity corresponding to the load complementary task and the comparison standard;

[0011] Analyze the normal analysis signal of the load, select the task with the highest priority to be executed, analyze the relevance of the remaining tasks to be executed, and generate control information based on the relevance and priority.

[0012] As a further solution of the present invention, the specific method of establishing the task-load mapping library is:

[0013] Based on the drone type, the corresponding historical execution tasks in its historical data are obtained, and the historical execution tasks are prioritized and scored based on four dimensions: urgency, timeliness, importance, and resource consumption. The corresponding weights are then combined to calculate the score value, which is then matched with the corresponding priority rating range to obtain high-priority tasks, medium-priority tasks, and low-priority tasks.

[0014] The loads corresponding to historical execution tasks are obtained, and a task-load mapping library is established.

[0015] As a further solution of the present invention, the specific method of generating the payload complementation and normal analysis signal is:

[0016] Obtain the pending tasks corresponding to the UAVs and label them as i, where i = 1, 2, ..., j, and determine the corresponding rating information. At the same time, obtain the payload requirements corresponding to the pending task i, and determine whether there are common payload requirements based on the payload requirements.

[0017] If there is a common load requirement, the corresponding task to be executed is obtained and recorded as a load complementary task, and a load complementary analysis signal is generated at the same time. Otherwise, if there is no common load requirement, a load normal analysis signal is generated.

[0018] As a further solution of the present invention, the specific method of analyzing the load complementary analysis signal is:

[0019] Obtain the load complementary tasks and judge their task priorities. If the two are tasks of the same type or continuous priority, generate a load secondary analysis signal. Otherwise, if the two are not tasks of the same type or continuous priority, generate an adjustment processing signal.

[0020] As a further solution of the present invention, the specific method of analyzing the adjustment processing signal is:

[0021] Obtain the highest priority task among the tasks to be executed, and obtain the corresponding load requirements, and at the same time obtain the maximum load and maximum carrying capacity corresponding to the drone, and calculate the remaining load and remaining carrying capacity based on the load requirements of the highest priority task, and then analyze the load requirements of the tasks to be executed based on the remaining load and remaining carrying capacity, and select the tasks to be executed that meet the requirements, and generate corresponding management and control information.

[0022] As a further solution of the present invention, the specific method of selecting the task to be executed that meets the requirements is:

[0023] According to the priority of the tasks to be analyzed, the analysis is carried out in sequence. The corresponding load and carrying capacity are compared with the remaining load and carrying capacity based on a group standard. If the remaining load and carrying capacity are satisfied, a satisfied analysis signal is generated. At the same time, the load complementary task of the selected tasks to be analyzed is judged. If it exists, it is not selected and the highest priority task is executed first. Otherwise, if it does not exist, the corresponding selected tasks to be analyzed are executed synchronously with the highest priority task, and control information is generated;

[0024] If the remaining load and carrying capacity are not met, the highest priority task is executed and control information is generated.

[0025] As a further solution of the present invention, the specific method of analyzing the load secondary analysis signal is:

[0026] Obtain the load complementation task, calculate its corresponding total load and total carrying capacity, determine the relationship between the total load, total carrying capacity and the maximum load, maximum carrying capacity. If the maximum load and maximum carrying capacity are met, generate control information based on the load complementation task. Otherwise, select the corresponding number of tasks to be analyzed in descending order according to their priority, execute them synchronously, and generate control information.

[0027] As a further solution of the present invention, the specific method of analyzing the normal load analysis signal is:

[0028] Arrange all pending tasks in descending order of priority, take the highest priority task as the benchmark, calculate the remaining load and carrying capacity after execution, and then analyze and process the remaining load and carrying capacity;

[0029] Obtain the remaining tasks to be executed and the corresponding load requirements, filter out the tasks to be executed that meet the requirements of load ≤ remaining load and load ≤ remaining load from the remaining tasks, and mark them as pre-selected tasks. At the same time, analyze the correlation between the pre-selected tasks and the highest priority tasks to be executed, select the tasks with the smallest correlation and the highest priority to be executed synchronously with the benchmark tasks, and generate management and control information.

[0030] The present invention provides a method for controlling the multi-task payload of an unmanned aerial vehicle (UAV). Compared with the existing technology, it has the following advantages:

[0031] The present invention quantitatively scores tasks from four dimensions: urgency, timeliness, importance, and resource consumption, calculates task priorities based on weights, and divides priority rating intervals. This can comprehensively and accurately assess the importance of tasks, ensure that high-priority tasks are executed first, and improve the timeliness and effectiveness of task execution. By establishing a task-payload mapping library, tasks are screened based on payload complementarity requirements to reduce duplicate payload loading. At the same time, resources are dynamically allocated based on the remaining payload and payload capacity of the drone, and tasks with low correlation and high priority are prioritized for synchronous execution, thereby improving payload resource utilization and reducing drone energy consumption and operating costs.

[0032] This invention generates different analysis signals based on payload complementarity and formulates differentiated task execution strategies for these signals, enabling dynamic task adjustment and collaborative optimization. Whether executing the same type of tasks in parallel or rationally combining tasks of varying priorities, flexible scheduling can be achieved based on the drone's resource status and task requirements, enhancing the system's adaptability and reliability in complex scenarios. The introduction of a task relevance assessment mechanism avoids resource conflicts and interference during task execution, further improving the coordination and efficiency of multi-tasking execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a diagram of the steps of the present invention. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0035] See also Figure 1 The present application provides a method for controlling multi-task payloads of a UAV, which specifically includes the following steps:

[0036] Step S1: Based on the drone type, the corresponding historical execution tasks in its historical data are obtained, and the historical execution tasks are prioritized to obtain high-priority tasks, medium-priority tasks, and low-priority tasks. The priority classification here is analyzed from four dimensions: urgency (weight 30%), timeliness (weight 25%), importance (weight 25%), and resource consumption (weight 20%). The above four dimensions are scored respectively, and their corresponding weights are calculated and matched with the corresponding priority rating intervals, as shown in the following priority rating interval table:

[0037]

[0038] Based on the priority rating interval, the corresponding priority information is generated, and the payloads corresponding to the historical execution tasks are obtained. For example, when the execution task is emergency rescue, the corresponding payload combination is "photoelectric camera + infrared thermal imager + lidar + communication relay"; when the execution task is power inspection, the corresponding payload combination is "high-definition camera + ultraviolet imager + infrared thermometer"; when the execution task is agricultural plant protection, the corresponding payload combination is "multispectral camera + pesticide sprayer + meteorological sensor". At the same time, a task-payload mapping library is established.

[0039] Step S2, obtain the pending tasks corresponding to the drone and label them as i, where i = 1, 2, ..., j, and rate their priorities to obtain rating information, then sort the pending tasks i from high to low according to the rating information, and at the same time obtain the load requirements corresponding to the pending tasks i, and judge whether there is complementarity based on the load requirements. The complementarity here means that the pending tasks have common load requirements. For example, Task A (forest fire monitoring) and Task B (wildlife tracking) both require infrared thermal imagers, which are regarded as load complementarity. If there is complementarity, the corresponding pending tasks are obtained and recorded as load complementarity tasks. Specifically, for load complementarity tasks, tasks with higher urgency are executed first. If the urgency is the same, tasks with higher timeliness are executed first. If they are still the same, they are sorted according to the time of task initiation, with the first initiator taking priority. At the same time, a load complementarity analysis signal is generated. Otherwise, if it does not exist, a load normal analysis signal is generated.

[0040] Step S3: Analyze the generated load complementation analysis signal to obtain the load complementation task and determine its task priority. If the two tasks are of the same type or continuous priority, generate a load secondary analysis signal. Conversely, if the two tasks are not of the same type or continuous priority, generate an adjustment processing signal. The same type or continuous priority tasks here indicate that the load complementation tasks are tasks of the same priority and can be executed simultaneously, while the continuous priority tasks indicate that the load complementation tasks are in a superior-subordinate relationship, specifically one task is a superior-subordinate relationship of the other task, and the two tasks are analyzed and processed separately.

[0041] After receiving the payload complementation analysis signal, the associated payload complementation task set is extracted. Based on the task priority rating results, the following logic is used to determine the task relationship:

[0042] Determination of tasks of the same type: If the tasks have the same priority level (all high / medium / low priority) and there is no conflict in the task execution time window, they are marked as tasks of the same type that can be executed in parallel.

[0043] Continuous priority task determination: If there is a direct hierarchical relationship between task priorities (for example, a high-priority task is a prerequisite for a medium-priority task, or a low-priority task can serve as a supplement to a high-priority task), it is marked as a continuous priority task.

[0044] Further, based on the above relationship, for tasks of the same type or with continuous priority, a load secondary analysis signal is generated, and for tasks of different types or with non-continuous priority, an adjustment processing signal is generated;

[0045] The generated adjustment processing signal is analyzed to obtain the highest priority task among the tasks to be executed, and the corresponding payload requirements are obtained. At the same time, the maximum load and maximum carrying capacity corresponding to the drone are obtained. The maximum load here represents the working time corresponding to the battery power. A specific payload requirement list (including payload type, quantity, power consumption parameters, and weight parameters) is used to calculate the theoretical maximum working time based on the drone battery capacity and the average power consumption of each payload (for example, if the battery capacity is 100Wh and the radar power consumption is 20W, the maximum load is 5 hours). At the same time, based on the drone's payload limit (such as maximum take-off weight - fuselage weight), the maximum weight of the payload that can be carried is determined, and the remaining load is calculated as: maximum load - expected power consumption of the highest priority task × execution time, and remaining carrying capacity = maximum carrying capacity - total weight of the highest priority task payload. Based on the payload requirements of the highest priority task, the remaining load and remaining carrying capacity are calculated. Then, the payload requirements of the tasks to be executed are analyzed based on the remaining load and remaining carrying capacity, and the tasks to be executed that meet the requirements are selected. At the same time, the corresponding control information is generated. The specific analysis method is as follows:

[0046] According to the priority of the task to be analyzed, the selection and analysis are carried out in sequence, and the corresponding load and carrying capacity are compared with the remaining load and carrying capacity based on a group as the standard. If the remaining load and carrying capacity are met, it means that the load and carrying capacity corresponding to the selected task to be analyzed are less than the remaining load and carrying capacity, then a satisfied analysis signal is generated, and the selected task to be analyzed is judged for load complementary tasks. If it exists, it is not selected, and the highest priority task is executed first. Otherwise, if it does not exist, the corresponding selected task to be analyzed is executed synchronously with the highest priority task, and the synchronous execution here means that the load requirements of the corresponding task to be analyzed are installed synchronously. When executing tasks, they are executed from high to low according to priority, and management information is generated. Each time a group of tasks is selected (it can be set to 3-5 tasks as a group to avoid excessive single resource occupation), and its total load and total carrying capacity are calculated. If the total load of a group of tasks ≤ the remaining load and the total carrying capacity ≤ the remaining carrying capacity, a satisfied analysis signal is generated, and further check whether there is a load complementary conflict between the group of tasks and the highest priority task:

[0047] If there is a conflict: skip the group of tasks and execute the highest priority task first.

[0048] If there is no conflict: the group of tasks will be executed simultaneously with the highest priority task, and control information will be generated including the task execution order and payload installation plan (for example, "install the radar first to perform high-priority reconnaissance, and simultaneously install the camera to perform medium-priority inspection");

[0049] If the remaining load and carrying capacity are not met, the highest priority task is executed and control information is generated.

[0050] After receiving the payload secondary analysis signal, the system summarizes the power consumption and weight of all payloads in the payload complementation task to obtain the total load and total carrying capacity, which are then compared with the maximum load and maximum carrying capacity. If the total load ≤ maximum load and the total carrying capacity ≤ maximum carrying capacity, it generates control information centered on the complementary tasks to support parallel execution (e.g., "simultaneously enable infrared thermal imaging cameras to perform forest fire monitoring and wildlife tracking tasks").

[0051] If not, some tasks will be selected in descending order of priority until the total resource demand meets the drone's carrying capacity, and control information containing the screened tasks will be generated.

[0052] Step S4: Analyze the generated load normal analysis signal, obtain all pending tasks, and sort them from high to low according to their corresponding priorities. Then, based on the pending task with the highest priority, obtain its corresponding load requirements, calculate the remaining load and remaining payload corresponding to the UAV, and then perform analysis and processing based on the remaining load and remaining payload.

[0053] Obtain the remaining tasks to be executed and the corresponding load requirements, then obtain the tasks to be executed that meet the remaining load and remaining carrying capacity, and mark them as pre-selected tasks. At the same time, analyze the correlation between the pre-selected tasks and the highest priority tasks to be executed, and the correlation here is determined by the impact between tasks. Then sort them from small to large according to the correlation, and make a comprehensive determination based on the priorities corresponding to the pre-selected tasks. Specifically, select the pre-selected task with the smallest correlation and the highest priority as the standard, and execute it synchronously with the highest priority task to be executed to generate management and control information.

[0054] Some of the data in the above formulas are calculated based on their numerical values ​​and are not substituted into parameter units for calculation. At the same time, the contents not described in detail in this specification belong to the existing technology known to those skilled in the art.

[0055] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A multi-task payload control method for an unmanned aerial vehicle, characterized in that: The method specifically comprises the following steps: Priority information is generated based on multi-dimensional priority classification of historical execution tasks, and a task-load mapping library is established in combination with corresponding load requirements; Determine the priority of the UAV's tasks to be executed, and select payload complementary tasks based on payload requirements, generating payload complementary and normal analysis signals; Process the load complementation analysis signal to determine whether the load complementation tasks are of the same type or have a continuous priority relationship, and generate load secondary analysis and adjustment processing signals; Analyze the adjustment processing signal to obtain the highest priority task and load requirement, calculate the remaining load and carrying capacity as comparison criteria, select the tasks to be analyzed that meet the criteria in order of priority, and generate control information; Analyze the secondary analysis signal of the load and generate control information based on the relationship between the total load and total carrying capacity corresponding to the load complementary task and the comparison standard; Analyze the normal analysis signal of the load, select the task with the highest priority to be executed, analyze the relevance of the remaining tasks to be executed, and generate control information based on the relevance and priority.

2. The multi-task load management method for unmanned aerial vehicles according to claim 1, characterized in that: The specific method of establishing the task-load mapping library is: Based on the type of drone, the corresponding historical execution tasks in its historical data are obtained, and the historical execution tasks are prioritized and classified. They are scored from four dimensions: urgency, timeliness, importance, and resource consumption. The corresponding weights are combined to calculate the score value, and the score value is matched with the corresponding priority rating interval to obtain high-priority tasks, medium-priority tasks, and low-priority tasks. The payloads corresponding to the historical execution tasks are obtained, and a task-payload mapping library is established.

3. The multi-task load management method for unmanned aerial vehicles according to claim 1, characterized in that: The specific method of generating the payload complement and normal analysis signals is as follows: Obtain the pending tasks corresponding to the UAV and label them as i, where i = 1, 2, ..., j, and determine the corresponding rating information. At the same time, obtain the payload requirements corresponding to the pending task i, and determine whether there are common payload requirements based on the payload requirements; If there is a common load requirement, the corresponding task to be executed is obtained and recorded as a load complementary task, and a load complementary analysis signal is generated at the same time. Otherwise, if there is no common load requirement, a load normal analysis signal is generated.

4. The multi-task load management method for unmanned aerial vehicles according to claim 1, characterized in that: The specific method of analyzing the load complementary analysis signal is: Obtain the load complementary tasks and judge their task priorities. If the two are tasks of the same type or continuous priority, generate a load secondary analysis signal. Otherwise, if the two are not tasks of the same type or continuous priority, generate an adjustment processing signal.

5. The multi-task load control method for unmanned aerial vehicles according to claim 1, characterized in that: The specific method of analyzing the adjustment processing signal is: Obtain the highest priority task among the tasks to be executed, and obtain the corresponding load requirements, and at the same time obtain the maximum load and maximum carrying capacity corresponding to the drone, and calculate the remaining load and remaining carrying capacity based on the load requirements of the highest priority task, and then analyze the load requirements of the tasks to be executed based on the remaining load and remaining carrying capacity, and select the tasks to be executed that meet the requirements, and generate corresponding management and control information.

6. A multi-task load control method for unmanned aerial vehicles according to claim 5, characterized in that: The specific method of selecting tasks to be executed that meet the requirements is: According to the priority of the tasks to be analyzed, the analysis is carried out in sequence. The corresponding load and carrying capacity are compared with the remaining load and carrying capacity based on a group standard. If the remaining load and carrying capacity are satisfied, a satisfied analysis signal is generated. At the same time, the load complementary task of the selected tasks to be analyzed is judged. If it exists, it is not selected and the highest priority task is executed first. Otherwise, if it does not exist, the corresponding selected tasks to be analyzed are executed synchronously with the highest priority task, and control information is generated; If the remaining load and carrying capacity are not met, the highest priority task is executed and control information is generated.

7. The method for controlling multi-task payload of an unmanned aerial vehicle according to claim 1, wherein: The specific method of analyzing the load secondary analysis signal is: Obtain the load complementation task, calculate its corresponding total load and total carrying capacity, determine the relationship between the total load, total carrying capacity and the maximum load, maximum carrying capacity. If the maximum load and maximum carrying capacity are met, generate control information based on the load complementation task. Otherwise, select the corresponding number of tasks to be analyzed in descending order according to their priority, execute them synchronously, and generate control information.

8. The method for controlling multi-task payload of an unmanned aerial vehicle according to claim 1, wherein: The specific method of analyzing the normal load analysis signal is as follows: Arrange all pending tasks in descending order of priority, take the highest priority task as the benchmark, calculate the remaining load and carrying capacity after execution, and then analyze and process the remaining load and carrying capacity; Obtain the remaining tasks to be executed and the corresponding load requirements, filter out the tasks to be executed that meet the requirements of load ≤ remaining load and load ≤ remaining load from the remaining tasks, and mark them as pre-selected tasks. At the same time, analyze the correlation between the pre-selected tasks and the highest priority tasks to be executed, select the tasks with the smallest correlation and the highest priority to be executed synchronously with the benchmark tasks, and generate management and control information.

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