Unmanned aerial vehicle multi-task load management method

Abstract

The application discloses a kind of unmanned vehicle multi-task load management and control method, the present application relates to unmanned vehicle load management and control technical field, it is difficult to solve the technical problem that task is comprehensively evaluated and optimized scheduling, leading to low efficiency of task execution, resource utilization is insufficient, the present application is quantified by from multiple dimensions, the priority of task is calculated in combination with weight, and corresponding priority is determined, can comprehensively and accurately evaluate task importance, by establishing task-load mapping library, task is screened based on load complementary demand, reduce repeated load carrying, in combination with the dynamic distribution resource of unmanned vehicle remaining load and load weight, the task with small correlation and high priority is preferentially selected to be executed synchronously, improve load resource utilization, reduce unmanned vehicle energy consumption and operating cost, introduce task correlation evaluation mechanism, avoid resource conflict and interference in task execution process, further improve the synergy and efficiency of multi-task execution.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) payload management technology, specifically to a method for managing multi-mission payloads of UAVs. Background Technology

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

[0003] Traditional UAV mission management typically employs 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, UAVs often face situations where multiple tasks coexist, task priorities change with the environment, and payload resources are limited. Existing technologies struggle to comprehensively evaluate and optimize scheduling based on multiple dimensions such as task urgency, timeliness, importance, and resource consumption, resulting in low task execution efficiency, insufficient resource utilization, and an inability to meet mission requirements in complex scenarios. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for managing multi-task payloads of unmanned aerial vehicles (UAVs), which solves the problem of low task execution efficiency and insufficient resource utilization caused by the difficulty in comprehensively evaluating and optimizing task scheduling.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for managing multi-task payloads of an unmanned aerial vehicle (UAV), which specifically includes the following steps:

[0006] Priority information is generated by classifying historical tasks into priority categories from multiple dimensions, and a task-load mapping library is established in conjunction with the corresponding load requirements.

[0007] The priority of the tasks to be performed by the UAV is determined, and complementary tasks are selected based on the payload requirements, generating complementary and normal analysis signals.

[0008] The load complementarity analysis signal is processed to determine whether the load complementarity tasks are of the same type or have a continuous priority relationship, and to generate load secondary analysis and adjustment processing signals.

[0009] The adjustment and processing signals are analyzed to obtain the highest priority tasks and load requirements, and the remaining load and carrying capacity are calculated and recorded as comparison standards. Tasks that meet the standards are selected in order of priority to generate control information.

[0010] The load secondary analysis signal is analyzed, and control information is generated based on the relationship between the total load and total load capacity corresponding to the load complementary task and the comparison standard.

[0011] The load normal analysis signal is analyzed, the highest priority task to be executed is selected, and the correlation of the remaining tasks to be executed is analyzed. The control information is generated by combining the correlation and priority.

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

[0013] Based on the type of drone, the historical execution tasks corresponding to its historical data are obtained. At the same time, the historical execution tasks are classified by priority and scored from four dimensions: urgency, timeliness, importance, and resource consumption. The scores are calculated by combining the corresponding weights and matched with the corresponding priority rating range to obtain high-priority tasks, medium-priority tasks, and low-priority tasks.

[0014] It also acquires the payloads corresponding to historically executed tasks and establishes a task-payload mapping library.

[0015] As a further aspect of the present invention, the specific method for generating the load complementarity and normal analysis signals is as follows:

[0016] Obtain the task to be executed corresponding to the UAV and label it as i, where i = 1, 2, ..., j, and determine the corresponding rating information. At the same time, obtain the load requirement corresponding to the task to be executed i, and determine whether there is a common load requirement based on the load requirement.

[0017] If there is a shared 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. Otherwise, if there is no shared load requirement, a load normal analysis signal is generated.

[0018] As a further aspect of the present invention, the specific method for analyzing the load complementarity analysis signal is as follows:

[0019] Acquire complementary load tasks and determine their task priorities. If the two tasks are of the same type or have consecutive priorities, generate a secondary load analysis signal. Otherwise, if the two tasks are not of the same type or have consecutive priorities, generate an adjustment processing signal.

[0020] As a further aspect of the present invention, the specific method for analyzing the adjustment processing signal is as follows:

[0021] The system retrieves the highest priority task from the pending tasks and its corresponding payload requirements. It also retrieves the maximum load and maximum carrying capacity of the UAV. Based on the payload requirements of the highest priority task, it calculates the remaining load and carrying capacity. Then, it analyzes the payload requirements of the pending tasks based on the remaining load and carrying capacity, selects the pending tasks that meet the requirements, and generates the corresponding control information.

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

[0023] According to the priority of the tasks to be analyzed, the analysis is performed in sequence. The corresponding load and capacity of a set are compared with the remaining load and capacity. If the remaining load and capacity are satisfied, a satisfactory analysis signal is generated. At the same time, the load complementary task is judged for the selected tasks to be analyzed. 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 and the highest priority task are executed synchronously, and control information is generated.

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

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

[0026] Obtain the load complementary tasks and calculate their corresponding total load and total capacity. Determine the relationship between the total load and total capacity and the maximum load and maximum capacity. If the maximum load and maximum capacity are met, then control information is generated based on the load complementary tasks. Otherwise, if the conditions are not met, then a corresponding number of tasks to be analyzed are selected in descending order of priority and executed synchronously, and control information is generated.

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

[0028] All tasks to be executed are sorted in descending order of priority. The remaining load and weight after execution are calculated based on the highest priority task. Then, the remaining load and weight are analyzed and processed.

[0029] Obtain the remaining tasks to be executed and their corresponding load requirements. Filter out the remaining tasks that meet the conditions of load ≤ remaining load and weight ≤ remaining weight and mark them as pre-selected tasks. At the same time, analyze the correlation between the pre-selected tasks and the highest priority task to be executed, select the task with the least correlation and the highest priority to be executed synchronously with the baseline task, and generate control information.

[0030] This invention provides a method for managing multi-mission payloads of unmanned aerial vehicles (UAVs). Compared with existing technologies, it has the following advantages:

[0031] This invention uses a quantitative scoring system based on four dimensions: urgency, timeliness, importance, and resource consumption. Combined with weighted calculations, task priorities are determined and priority rating intervals are defined. This comprehensive and accurate assessment of task importance ensures high-priority tasks are executed first, improving the timeliness and effectiveness of task execution. By establishing a task-payload mapping library, tasks are selected based on complementary payload requirements, reducing redundant payload loads. Simultaneously, resources are dynamically allocated based on the drone's remaining payload and weight, prioritizing tasks with low relevance and high priority for simultaneous execution. This improves payload resource utilization and reduces drone energy consumption and operating costs.

[0032] This invention generates different analysis signals based on payload complementarity and formulates differentiated task execution strategies for different signals, achieving dynamic adjustment and collaborative optimization of tasks. Whether it is parallel execution of similar tasks or a reasonable combination of tasks with different priorities, it can flexibly schedule tasks according to the UAV resource status and task requirements, enhancing the system's adaptability and reliability in complex scenarios. It also introduces a task correlation assessment mechanism to avoid resource conflicts and interference during task execution, further improving the coordination and efficiency of multi-task execution. Attached Figure Description

[0033] Figure 1 This is a diagram illustrating the steps and methods of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Please see Figure 1 This application provides a method for managing multi-task payloads of an unmanned aerial vehicle (UAV), which specifically includes the following steps:

[0036] Step S1: Based on the drone type, obtain the corresponding historical execution tasks from its historical data. Simultaneously, classify these historical execution tasks by priority to obtain high-priority, medium-priority, and low-priority tasks. This priority classification is analyzed from four dimensions: urgency (30% weight), timeliness (25% weight), importance (25% weight), and resource consumption (20% weight). Each of these four dimensions is scored, and the scores are calculated based on their respective weights. This is then matched with the corresponding priority rating intervals, as shown in the priority rating interval table below:

[0037]

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

[0039] Step S2: Obtain the tasks to be executed for the UAV and label them as i, where i = 1, 2, ..., j. Rate their priority and obtain rating information. Then, sort the tasks to be executed i from high to low according to the rating information. At the same time, obtain the payload requirements corresponding to the tasks to be executed i and determine whether there is a complementary situation based on the payload requirements. Here, complementarity means that the tasks to be executed have common payload requirements. For example, both Task A (forest fire monitoring) and Task B (wildlife tracking) require infrared thermal imagers, which are considered to be complementary payloads. If there is complementarity, obtain the corresponding tasks to be executed and record them as complementary payload tasks. Specifically, for complementary payload tasks, prioritize the task with higher urgency. If the urgency is the same, prioritize the task with higher timeliness. If they are still the same, sort them according to the order of task initiation time, with the first initiator having priority. At the same time, generate a complementary payload analysis signal. Otherwise, if there is no complementarity, generate a normal payload analysis signal.

[0040] Step S3: Analyze the generated load complementarity analysis signal to obtain the load complementarity task and determine its task priority. If the two are the same type or consecutive priority tasks, generate a load secondary analysis signal. Otherwise, if the two are not the same type or consecutive priority tasks, generate an adjustment processing signal. Here, the same type or consecutive priority tasks mean that the load complementarity tasks belong to the same priority tasks and can be executed at the same time. The consecutive priority tasks mean that the load complementarity tasks have a superior-subordinate priority relationship, and one of the tasks is the superior-subordinate relationship of the other task. Analyze and process the two separately.

[0041] After receiving the load complementarity analysis signal, the associated load complementarity task set is extracted. Based on the task priority rating results, the task relationships are determined using the following logic:

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

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

[0044] Furthermore, for the above relationships, a secondary load analysis signal is generated for tasks of the same type or with consecutive priority, and an adjustment processing signal is generated for tasks of different types or with non-consecutive priority.

[0045] The generated adjustment signals are analyzed to obtain the highest priority task among the pending tasks and its corresponding payload requirements. The maximum load and maximum carrying capacity of the UAV are also obtained, where the maximum load represents the operating time corresponding to the battery level. A detailed payload requirement list (including payload type, quantity, power consumption parameters, and weight parameters) is provided. Based on the UAV's battery capacity and the average power consumption of each payload, the theoretical maximum operating time is calculated (e.g., if the battery capacity is 100Wh and the radar power consumption is 20W, the maximum load is 5 hours). Simultaneously, based on the UAV's weight limitations (e.g., maximum takeoff weight - fuselage weight), the maximum weight of the payload is determined, and the remaining load is calculated as follows: Remaining load = Maximum load - Estimated power consumption of the highest priority task × Execution time, and Remaining carrying capacity = Maximum carrying capacity - Total weight of the highest priority task's payload. Based on the payload requirements of the highest priority task, the remaining load and remaining carrying capacity are calculated. Then, using the remaining load and remaining carrying capacity as standards, the payload requirements of the pending tasks are analyzed, and tasks that meet the requirements are selected. Corresponding control information is generated, and the specific analysis method is as follows:

[0046] Based on the priority of the tasks to be analyzed, they are selected and analyzed sequentially. A group is used as a standard, and its corresponding load and capacity are compared with the remaining load and capacity. If the remaining load and capacity are met (meaning the load and capacity of the selected task are less than the remaining load and capacity), a satisfactory analysis signal is generated. Simultaneously, a load complementarity judgment is performed on the selected tasks. If one exists, it is not selected, and the highest priority task is executed first. Otherwise, if no complementarity exists, the corresponding selected task and the highest priority task are executed synchronously. Synchronous execution here means that the load requirements of the corresponding task are installed synchronously. During task execution, tasks are executed from high to low priority, and control information is generated. Each time, a group of tasks is selected (3-5 tasks can be set as a group to avoid excessive resource consumption in a single run), and its total load and total capacity are calculated. If the total load of a group of tasks is less than or equal to the remaining load and the total capacity is less than or equal to the remaining capacity, a satisfactory analysis signal is generated. Further checks are then performed to determine if there is a load complementarity conflict between this group of tasks and the highest priority task.

[0047] If a conflict exists: skip the task group and execute the highest priority task first.

[0048] If there is no conflict: execute this group of tasks synchronously with the highest priority task, and generate control information including the task execution order and payload installation plan (e.g., "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 capacity are not met, the highest priority task is executed, and control information is generated.

[0050] After receiving the load secondary analysis signal, the total load and total load capacity of all loads in the load complementary task are summarized to obtain the total load and total load capacity. The total load and total load capacity are compared with the maximum load and the maximum load capacity. If the total load is less than or equal to the maximum load and the total load capacity is less than or equal to the maximum load capacity, control information with the complementary task as the core is generated to support parallel execution (such as "simultaneously enable the infrared thermal imager to perform forest fire monitoring and wildlife tracking tasks").

[0051] If the requirements are not met, select some tasks in descending order of priority until the total resource requirements meet the drone's carrying capacity, and generate control information containing the selected tasks.

[0052] Step S4: Analyze the generated load normal analysis signal, obtain all tasks to be executed, and sort them from high to low according to their corresponding priorities. Then, take the task to be executed with the highest priority as the standard, obtain its corresponding load requirement, and calculate the UAV's remaining load and remaining payload. Then, analyze and process the remaining load and remaining payload.

[0053] The remaining tasks to be executed and their corresponding load requirements are obtained. Then, tasks that meet the remaining load and weight requirements are obtained and marked as pre-selected tasks. At the same time, the correlation between the pre-selected tasks and the highest priority tasks to be executed is analyzed. The correlation is determined by the influence between tasks. Then, the tasks are sorted from smallest to largest according to their correlation and combined with the priority of the corresponding tasks to make a comprehensive determination. Specifically, the pre-selected task with the smallest correlation and the highest priority is selected as the standard and executed synchronously with the highest priority tasks to be executed, generating control information.

[0054] The data in the above formulas are all calculated using numerical values, without substituting the units of the parameters. In addition, the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0055] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for managing and controlling multi-task loads of a UAV, characterized in that, The method specifically comprises the following steps: According to the historical execution task, priority classification is performed from multiple dimensions to generate priority information, and a task-load mapping library is established in combination with the corresponding load demand; The priority of the unmanned aerial vehicle to be executed task is determined, and a load complementary task is screened based on the load demand to generate a load complementary and normal analysis signal; The load complementary analysis signal is processed to determine whether the load complementary task is of the same type or has a continuous priority relationship, and a load secondary analysis and adjustment processing signal is generated; The adjustment processing signal is analyzed to obtain the highest priority task and the load demand, and the residual load and the carrying capacity are calculated as a comparison standard. The tasks to be analyzed that meet the standard are selected in turn according to the priority, and control information is generated; The load secondary analysis signal is analyzed according to the size relationship between the total load and the total carrying capacity corresponding to the load complementary task and the comparison standard, and control information is generated; The load normal analysis signal is analyzed, the highest priority task to be executed is selected, and the correlation of the remaining to-be-executed tasks is analyzed. The control information is generated by comprehensively considering the correlation and the priority. 2.The UAV multi-mission payload management method of claim 1, wherein, The specific way of establishing the task-load mapping library is: Based on the type of the unmanned aerial vehicle, the corresponding historical execution task in the historical data is obtained, and the historical execution task is classified by priority. The score value is calculated by scoring from four dimensions of urgency, timeliness, importance and resource consumption, and matching the corresponding priority rating interval to obtain high-priority tasks, medium-priority tasks and low-priority tasks. The corresponding load of the historical execution task is obtained, and a task-load mapping library is established. 3.The unmanned aerial vehicle multi-task load management method of claim 1, wherein, The specific way of generating the load complementary and normal analysis signal is: The to-be-executed task corresponding to the unmanned aerial vehicle is obtained and labeled as i, and i=1, 2, …, j. The corresponding rating information is determined, and the load demand corresponding to the to-be-executed task i is obtained. Whether there is a common load demand is determined according to the load demand; If there is a common load demand, the corresponding to-be-executed task is obtained, which is recorded as a load complementary task. A load complementary analysis signal is generated. Otherwise, if there is no common load demand, a load normal analysis signal is generated.

4. The unmanned aerial vehicle multi-mission payload management method of claim 1, wherein, The specific way of analyzing the load complementary analysis signal is: The load complementary task is obtained, and the priority of the task is determined. If the two are of the same type or have a continuous priority task, a load secondary analysis signal is generated. Otherwise, if the two are not of the same type or have a continuous priority task, an adjustment processing signal is generated.

5. The unmanned aerial vehicle multi-mission payload management method of claim 1, wherein, The specific way of analyzing the adjustment processing signal is: The highest priority task in the to-be-executed task is obtained, and the corresponding load demand is obtained. The maximum load and the maximum carrying capacity corresponding to the unmanned aerial vehicle are obtained. Based on the load demand of the highest priority task, the residual load and the residual carrying capacity are calculated. The load demand of the to-be-executed task is analyzed by taking the residual load and the residual carrying capacity as a standard, and the to-be-executed task that meets the demand is selected, and the corresponding control information is generated.

6. The unmanned aerial vehicle multi-mission payload management method of claim 5, wherein, The specific way of selecting the to-be-executed task that meets the demand is: According to the priority of the to-be-analyzed task, the selection analysis is sequentially performed, a set of standard is compared with the corresponding load and carrying capacity and the residual load and carrying capacity, if the residual load and carrying capacity are met, the analysis signal meeting the analysis is generated, at the same time, the selected to-be-analyzed task is judged for the load complementary task, if the load complementary task exists, the highest priority task is executed preferentially, otherwise, if the load complementary task does not exist, the selected to-be-analyzed task is synchronously executed with the highest priority task, and the management and control information is generated; If the residual load and carrying capacity are not met, the highest priority task is executed, and the management and control information is generated.

7. The unmanned aerial vehicle multi-mission payload management method of claim 1, wherein, The specific manner of analyzing the load secondary analysis signal is: The load complementary task is acquired, the corresponding total load and total carrying capacity are calculated, the relationship between the total load, the total carrying capacity and the maximum load, the maximum carrying capacity is judged, if the maximum load, the maximum carrying capacity are met, the management and control information is generated with the load complementary task as the standard, otherwise, if the maximum load, the maximum carrying capacity are not met, the corresponding number of to-be-analyzed tasks are sequentially selected according to the priority of the to-be-analyzed task from high to low for synchronous execution, and the management and control information is generated. 8.The unmanned aerial vehicle multi-task load management method of claim 1, wherein, The specific manner of analyzing the load normal analysis signal is: All to-be-executed tasks are arranged in descending order according to the priority, the residual load and the residual carrying capacity after execution are calculated with the highest priority task as the benchmark, then the residual load and the residual carrying capacity are analyzed and processed; The residual to-be-executed tasks and the corresponding load demand are acquired, the to-be-executed tasks meeting the conditions of load≤residual load and carrying capacity≤residual carrying capacity are selected from the residual tasks and marked as preselected tasks, at the same time, the correlation between the preselected tasks and the highest priority to-be-executed task is analyzed, the task with the minimum correlation and the highest priority is selected and synchronously executed with the benchmark task, and the management and control information is generated.

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