Unmanned aerial vehicle cluster control system

Abstract

The invention provides an unmanned aerial vehicle cluster control system, and relates to the field of unmanned aerial vehicles, and the system comprises a task information module which is used for obtaining the task information of a task; the equipment information module is used for acquiring equipment information of the unmanned equipment; the equipment scheme module is used for determining a task demand equipment scheme; the utilization coefficient module is used for determining a load utilization coefficient and a power utilization coefficient of the task demand equipment scheme; the scheduling coefficient module is used for determining a task priority scheduling coefficient according to the task basic information, the load utilization coefficient and the power utilization coefficient; the scheduling sequence module is used for determining a priority scheduling task; the operation coefficient module is used for determining the equipment health operation coefficient of the scheduled equipment; the fault recovery module is used for determining a fault recovery scheduling result according to the equipment healthy operation coefficient; and the scheduling strategy module is used for determining a scheduling strategy. According to the invention, the utilization rate of unmanned equipment resources and the completion rate of tasks can be improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicles, and particularly to a control system for an unmanned aerial vehicle cluster. Background Art

[0002] In the related art, the scheduling of an unmanned aerial vehicle cluster mainly relies on the manual scheduling method, that is, mainly depending on human factors. Over-reliance on human factors may make it difficult to accurately match the capabilities of unmanned devices, resulting in the failure of unmanned devices to fully exert their effectiveness, high energy consumption, and insufficient fault recovery mechanisms.

[0003] The information disclosed in the background art part of this application is only intended to deepen the understanding of the general background art of this application, and should not be regarded as an admission or an implication in any form that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0004] The present invention provides a control system for an unmanned aerial vehicle cluster, which can solve the technical problems in the related art that the capabilities of unmanned devices cannot be accurately matched, resulting in the failure of unmanned devices to fully exert their effectiveness, high energy consumption, and insufficient fault recovery mechanisms.

[0005] According to a first aspect of the present invention, there is provided a control system for an unmanned aerial vehicle cluster, including: A task information module, configured to obtain task information of a task at multiple moments in a scheduling period, where the task information includes: task basic information and task requirement information; A device information module, configured to obtain device information of an unmanned device at multiple moments in a scheduling period, where the device information includes: device status information and device function information; A device solution module, configured to determine a task requirement device solution according to the task requirement information and the device information; A utilization coefficient module, configured to determine a payload utilization coefficient and a power utilization coefficient of the task requirement device solution; A scheduling coefficient module, configured to determine a task priority scheduling coefficient according to the task basic information, the payload utilization coefficient, and the power utilization coefficient; A scheduling order module, configured to determine a preferentially scheduled task according to the task priority scheduling coefficient; An operation coefficient module, configured to determine a device healthy operation coefficient of a scheduled device according to the device status information; A fault recovery module, configured to determine a fault recovery scheduling result according to the device healthy operation coefficient; A scheduling strategy module, configured to determine a scheduling strategy according to the preferentially scheduled task and the fault recovery scheduling result.

[0006] According to the present invention, a task requirement device solution is determined based on the task requirement information and the device information, including: Determine the task requirement payload and the task requirement power according to the task requirement information; Determine the device idle state according to the device status information; Determine the schedulable devices according to the device idle state; Determine the schedulable unmanned device payload and the schedulable unmanned device power of multiple schedulable devices according to the device function information; Determine the task requirement device solution according to the task requirement payload, the task requirement power, the schedulable unmanned device payload, and the schedulable unmanned device power.

[0007] According to the present invention, a payload utilization coefficient and a power utilization coefficient of the task requirement device solution are determined, including: Determine the expected device payload and the expected device power of the task requirement device solution; Determine the payload utilization coefficient of the task requirement device solution according to the expected device payload and the task requirement payload; Determine the power utilization coefficient of the task requirement device solution according to the expected device power and the task requirement power.

[0008] According to the present invention, a task priority scheduling coefficient is determined based on the task basic information, the payload utilization coefficient, and the power utilization coefficient, including: Determine the total schedulable payload and the total schedulable power according to the schedulable unmanned device payload and the schedulable unmanned device power; Determine the expected task duration according to the task basic information; Determine the task priority according to the task basic information; Determine the task priority scheduling coefficient according to the task requirement payload, the task requirement power, the task priority, the expected task duration, the total schedulable payload, the total schedulable power, the payload utilization coefficient, and the power utilization coefficient.

[0009] According to the present invention, a task priority scheduling coefficient is determined based on the task requirement payload, the task requirement power, the task priority, the expected task duration, the total schedulable payload, the total schedulable power, the payload utilization coefficient, and the power utilization coefficient, including: According to the formula

[0010] Determine the task priority scheduling coefficient of the k-th task at the i-th moment in the scheduling period , wherein, and are preset weight values, if is a conditional function, and or is a logical operator of "or", is the task priority of the k-th task, is the load utilization factor of the k-th task at the i-th moment of the scheduling period, is the power utilization factor of the k-th task at the i-th moment of the scheduling period, is the task required load amount of the k-th task, is the task required power of the k-th task, is the expected task duration of the k-th task, is the total schedulable load amount at the i-th moment of the scheduling period, is the total schedulable power at the i-th moment of the scheduling period, K is the number of tasks at the i-th moment of the scheduling period, k ≤ K, and both k and K are positive integers.

[0011] According to the present invention, according to the device status information, determining the device healthy operation coefficient of the scheduled device includes: According to the device status information, determining the device real-time power, device temperature, and device signal strength; According to the device real-time power, determining the device battery life recognition result; According to the device temperature, determining the device temperature recognition result; According to the device signal strength, determining the device signal recognition result; According to the device battery life recognition result, the device temperature recognition result, and the device signal recognition result, determining the device healthy operation coefficient of the scheduled device.

[0012] According to the present invention, according to the device real-time power, determining the device battery life recognition result includes: Obtaining the initial power of the unmanned device; According to the initial power and the device real-time power, determining the consumed power; Determining the start scheduling time of the unmanned device; According to the start scheduling time, determining the scheduled time; According to the consumed power and the scheduled time, determining the actual power consumption speed; According to the expected task duration and the initial power, determining the expected power consumption speed; According to the actual power consumption speed and the expected power consumption speed, determining the power consumption recognition result; According to the device real-time power and a preset device power threshold, determining the device power recognition result; Determine the device battery life identification result according to the power consumption identification result and the device power identification result.

[0013] According to the present invention, determining the device temperature identification result according to the device temperature includes: Perform fitting according to the device temperature and the time in the scheduling period to obtain a device temperature function of the device temperature in the scheduling period; Determine the device temperature derivative function according to the device temperature function; Determine the device temperature change rate at multiple times in the scheduling period according to the device temperature derivative function; Determine the device temperature identification result according to the device temperature and the device temperature change rate.

[0014] According to the present invention, determining the device temperature identification result according to the device temperature and the device temperature change rate includes: Obtain the ambient temperature at multiple times in the scheduling period according to the temperature sensor arranged on the unmanned device; Determine the preset temperature threshold according to the ambient temperature; Determine the first temperature identification result according to the preset temperature threshold and the device temperature; Determine the second temperature identification result according to the device temperature change rate and the preset device temperature change rate threshold; Determine the device temperature identification result according to the first temperature identification result and the second temperature identification result.

[0015] According to the second aspect of the present invention, there is provided a method for controlling a drone swarm, including: Obtain the task information of the task at multiple times in the scheduling period, where the task information includes: task basic information and task requirement information; Obtain the device information of the unmanned device at multiple times in the scheduling period, where the device information includes: device status information and device function information; Determine the task requirement device plan according to the task requirement information and the device information; Determine the payload utilization coefficient and the power utilization coefficient of the task requirement device plan; Determine the task priority scheduling coefficient according to the task basic information, the payload utilization coefficient and the power utilization coefficient; Determine the priority scheduling task according to the task priority scheduling coefficient; Determine the device healthy operation coefficient of the scheduled device according to the device status information; Determine the fault recovery scheduling result according to the device healthy operation coefficient; Determine a scheduling strategy according to the priority scheduling task and the fault recovery scheduling result.

[0016] Technical effect: According to the present invention, task information and device information of the unmanned device can be accurately obtained, and according to the device information and task information, a device requirement plan for each task can be determined. Further, the resource utilization status of the device requirement plan for each task can be analyzed, and according to the resource utilization status and task information, the scheduling order of each task can be determined. On the other hand, during the scheduling process, the healthy working status of the unmanned device can be monitored and analyzed in real time, and according to the healthy working status, it can be determined whether a fault recovery scheduling for the unmanned device is required, improving the utilization rate of unmanned device resources and the task completion rate. When determining the task priority scheduling coefficient, the task priority scheduling coefficient can be determined according to the task demand load, task demand power, task priority, expected task duration, total schedulable load, total schedulable power, load utilization coefficient, and power utilization coefficient. During the calculation process, the task priority scheduling coefficient can be determined according to three aspects of the preset priority status, resource usage status, and resource overload status, improving the utilization rate of unmanned device resources while meeting the urgent needs of the task, and reducing the occurrence of deadlock phenomena of unmanned device resources, improving the comprehensiveness and accuracy of the task priority scheduling coefficient.

[0017] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and do not limit the present invention. According to the following detailed description of exemplary embodiments with reference to the accompanying drawings, other features and aspects of the present invention will be clearer. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other embodiments can be obtained based on these drawings without creative efforts. Figure 1 Exemplarily shows a block diagram of a drone swarm control system according to an embodiment of the present invention; Figure 2 Exemplarily shows a schematic flow chart of a drone swarm control method according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are only a part rather than all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] The technical solutions of the present invention will be described in detail below with specific embodiments. These specific embodiments may be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0021] Figure 1 Exemplarily shown is a block diagram of a UAV swarm control system according to an embodiment of the present invention. The system includes: A task information module, configured to obtain task information of a task at multiple moments in a scheduling period, where the task information includes: task basic information and task requirement information; A device information module, configured to obtain device information of unmanned devices at multiple moments in a scheduling period, where the device information includes: device status information and device function information; A device solution module, configured to determine a task requirement device solution according to the task requirement information and the device information; A utilization coefficient module, configured to determine a payload utilization coefficient and a power utilization coefficient of the task requirement device solution; A scheduling coefficient module, configured to determine a task priority scheduling coefficient according to the task basic information, the payload utilization coefficient, and the power utilization coefficient; A scheduling order module, configured to determine a prioritized scheduling task according to the task priority scheduling coefficient; An operation coefficient module, configured to determine a device healthy operation coefficient of the scheduled devices according to the device status information; A fault recovery module, configured to determine a fault recovery scheduling result according to the device healthy operation coefficient; A scheduling strategy module, configured to determine a scheduling strategy according to the prioritized scheduling task and the fault recovery scheduling result.

[0022] The UAV swarm control system according to an embodiment of the present invention can accurately obtain task information and device information of unmanned devices, and determine a device requirement plan for each task according to the device information and the task information. Further, it can analyze the resource utilization status of the device requirement plan for each task, and determine the scheduling order of each task according to the resource utilization status and the task information. On the other hand, during the scheduling process, it can monitor and analyze the healthy working status of unmanned devices in real time, and determine whether to perform a recovery schedule for the faults of unmanned devices according to the healthy working status, improving the utilization rate of unmanned device resources and the completion rate of tasks.

[0023] According to an embodiment of the present invention, in the task information module, it is used to obtain the task information of a task at multiple moments in a scheduling period, where the task information includes: task basic information and task requirement information. For example, the scheduling period refers to the time period for scheduling and executing a task, such as 24 hours, and the time interval between two adjacent moments in the scheduling period is 5 minutes.

[0024] For example, in the task information module, the task content of a task can be recorded when the task is released, and the task basic information (such as the expected task duration and task priority) and task requirement information (requirements to be met during the task execution, such as the task requirement payload) of the task can be determined according to the task content.

[0025] According to an embodiment of the present invention, in the device information module, it is used to obtain the device information of unmanned devices at multiple moments in a scheduling period, where the device information includes: device status information and device function information.

[0026] For example, according to the sensors installed in unmanned devices (such as UAVs), the device status information (such as the real-time power of the device and the device temperature) is obtained in real time, and the device function information (such as the payload of the unmanned device and the power of the unmanned device) is determined according to the factory information of the unmanned device.

[0027] According to an embodiment of the present invention, in the device plan module, it is used to determine a task requirement device plan according to the task requirement information and the device information.

[0028] According to an embodiment of the present invention, determining a task requirement device plan according to the task requirement information and the device information includes: Determining the task requirement payload and task requirement power according to the task requirement information; Determining the device idle status according to the device status information; Determining the schedulable devices according to the device idle status; Determine the schedulable payload and schedulable power of multiple schedulable devices based on the device function information. Determine the task requirement device plan based on the task requirement payload, the task requirement power, the schedulable payload of the schedulable device, and the schedulable power of the schedulable device.

[0029] For example, based on the task requirement information, determine the task requirement payload and the task requirement power. For example, for Task B that needs to transport an object of 1000 kg and requires a drone with a power of 50 kW to perform a stable inspection task in a harsh environment (such as typhoon, heavy snow), the task requirement payload and the task requirement power of Task B are 1000 kg and 50 kW respectively. Based on the device status information, determine the device idle status, which includes two types: "idle" and "scheduled". Determine the idle devices as schedulable devices. Based on the device function information of the schedulable devices, determine the schedulable payload and the schedulable power of each schedulable device. Based on the task requirement payload, the task requirement power, the schedulable payload of the schedulable device, and the schedulable power of the schedulable device, determine the task requirement device plan. For example, for Task A with a task requirement payload of 1000 kg, when there is a schedulable device with a schedulable payload of 1000 kg, a drone with a payload of 1000 kg can be assigned to this task. When there is no schedulable device with a schedulable payload of 1000 kg, among the schedulable drones, select the drone whose payload is closest to and greater than the task requirement payload. For example, a drone with a payload of 1100 kg can be assigned to this task.

[0030] According to an embodiment of the present invention, in the utilization coefficient module, it is used to determine the payload utilization coefficient and the power utilization coefficient of the task requirement device plan.

[0031] According to an embodiment of the present invention, determining the payload utilization coefficient and the power utilization coefficient of the task requirement device plan includes: Determine the expected device payload and the expected device power of the task requirement device plan. Based on the expected device payload and the task requirement payload, determine the payload utilization coefficient of the task requirement device plan. Based on the expected device power and the task requirement power, determine the power utilization coefficient of the task requirement device plan.

[0032] For example, determine the expected equipment load and expected equipment power of the task requirement equipment plan. For example, according to the task requirement equipment plan, a drone with a load of 1100 kg is assigned to Task A, and a drone with a power of 60 kw for stable inspection tasks in harsh environments. Then, the expected equipment load and expected equipment power of this task requirement equipment plan are 1100 kg and 60 kw respectively, and the task requirement load and task requirement power of this task are 1000 kg and 50 kw. Determine the load utilization factor of the task requirement equipment plan according to the ratio of the task requirement load to the expected equipment load; determine the power utilization factor of the task requirement equipment plan according to the ratio of the task requirement power to the expected equipment power.

[0033] According to an embodiment of the present invention, in the scheduling coefficient module, it is used to determine the task priority scheduling coefficient according to the task basic information, the load utilization factor, and the power utilization factor.

[0034] According to an embodiment of the present invention, determining the task priority scheduling coefficient according to the task basic information, the load utilization factor, and the power utilization factor includes: Determine the total schedulable load and total schedulable power according to the schedulable unmanned equipment load and the schedulable unmanned equipment power; Determine the expected task duration according to the task basic information; Determine the task priority according to the task basic information; Determine the task priority scheduling coefficient according to the task requirement load, the task requirement power, the task priority, the expected task duration, the total schedulable load, the total schedulable power, the load utilization factor, and the power utilization factor.

[0035] For example, determine the total schedulable load and total schedulable power according to the sum of the schedulable unmanned equipment loads and schedulable unmanned equipment powers of each schedulable device; determine how long it takes to complete the expected task according to the task basic information, that is, the expected task duration; determine the task priority of the task according to the task basic information. When the task is released, the priority of task scheduling is classified. The task priority is an integer from 1 to 10, and the larger the task priority, the more priority the task is scheduled; further analyze and evaluate the priority status of task scheduling according to the task requirement load, task requirement power, task priority, expected task duration, the total schedulable load, total schedulable power, load utilization factor, and power utilization factor, and determine the task priority scheduling coefficient.

[0036] According to an embodiment of the present invention, determining a task priority scheduling coefficient according to the task required load, the task required power, the task priority, the expected task duration, the total schedulable load, the total schedulable power, the load utilization coefficient, and the power utilization coefficient includes: determining the task priority scheduling coefficient of the k-th task at the i-th moment of the scheduling period according to formula (1) , (1) Wherein, and are preset weights, if is a conditional function, or is a logical operator of "or", is the task priority of the k-th task, is the load utilization coefficient of the k-th task at the i-th moment of the scheduling period, is the power utilization coefficient of the k-th task at the i-th moment of the scheduling period, is the task required load of the k-th task, is the task required power of the k-th task, is the expected task duration of the k-th task, is the total schedulable load at the i-th moment of the scheduling period, is the total schedulable power at the i-th moment of the scheduling period, K is the number of tasks at the i-th moment of the scheduling period, k ≤ K, and both k and K are positive integers.

[0037] According to an embodiment of the present invention, is the relative difference between the task priority of the k-th task and the average task priority of K tasks. The larger this ratio is, the relatively larger the task priority of the k-th task is.

[0038] According to an embodiment of the present invention, is the ratio of the task required load of the k-th task to the average task required load of K tasks. The larger this ratio is, the relatively larger the task required load of the k-th task is, is the ratio of the task required power of the k-th task to the average task required power of K tasks. The larger this ratio is, the relatively larger the task required power of the k-th task is, is the ratio of the expected task duration of the k-th task to the average expected task duration of K tasks. The larger this ratio is, the relatively longer the expected task duration of the k-th task is and the relatively longer the resource occupation duration is, is to determine the resource occupation amount of the k-th task according to the task required load and task required power of the k-th task, To determine the resource occupancy level of the k-th task based on the resource occupancy amount and resource occupancy duration of the k-th task It is the sum of the payload utilization factor and the power utilization factor of the k-th task at the i-th moment of the scheduling period, representing the resource utilization level of the k-th task at the i-th moment of the scheduling period It is the ratio of the sum of the resource utilization level of the k-th task at the i-th moment of the scheduling period and the resource occupancy level of the k-th task to the resource occupancy level of the k-th task. The larger this ratio is, the higher the resource utilization level of the k-th task at the i-th moment of the scheduling period. When priority is given to allocating unmanned equipment resources to this task, the resulting resource waste is smaller. The larger this ratio is, the lower the resource occupancy level of the k-th task. When priority is given to allocating unmanned equipment resources to this task, the unmanned equipment resources can be fully utilized, and the situation where one task occupies a large amount of unmanned equipment resources for a long time, resulting in other tasks being unable to execute, can be avoided

[0039] According to an embodiment of the present invention, in formula (1), the conditional function has the following two cases. When the condition of is satisfied, it indicates that there is a phenomenon that the task demand payload amount exceeds the total schedulable payload amount or the task demand power exceeds the total schedulable power, and the value of the conditional function is -10 , when the condition of is not satisfied, the value of the conditional function is 0

[0040] According to an embodiment of the present invention represents determining the task priority scheduling coefficient based on three aspects: the preset priority status, resource usage status, and resource overload status. Among them is much greater than , and both are positive integers. It means that when determining the task priority scheduling coefficient, the task priority is considered first, and resources are preferentially scheduled for tasks with a larger task priority to meet the urgent needs of the tasks. When the task priorities are the same, the resource usage status of the tasks is considered, and resources are preferentially scheduled for tasks with a lower resource occupancy level and a higher resource utilization level can be set to 1000 can be set to 1. When a resource overload situation occurs, the value of the conditional function is -10 . Since the maximum value of the task priority value is 10, then is less than 10 , and is much greater than , when a resource overload condition occurs, the value of the task priority scheduling coefficient is much less than 0, avoiding the deadlock phenomenon of unmanned device resources that occurs when a task with too high a priority requires too many unmanned device resources and the schedulable resources cannot meet the task requirements.

[0041] In this way, the task priority scheduling coefficient can be determined according to the task demand payload, task demand power, task priority, expected task duration, total schedulable payload, total schedulable power, payload utilization coefficient, and power utilization coefficient. During the calculation process, the task priority scheduling coefficient can be determined according to three aspects: the preset priority status, resource usage status, and resource overload status, improving the utilization rate of unmanned device resources while meeting the urgent needs of the task, reducing the occurrence of the deadlock phenomenon of unmanned device resources, and enhancing the comprehensiveness and accuracy of the task priority scheduling coefficient.

[0042] According to an embodiment of the present invention, in the scheduling order module, it is used to determine the priority scheduling task according to the task priority scheduling coefficient.

[0043] For example, the task corresponding to the maximum value of the task priority scheduling coefficients of all tasks is determined as the priority scheduling task.

[0044] According to an embodiment of the present invention, in the operation coefficient module, it is used to determine the device healthy operation coefficient of the scheduled device according to the device status information.

[0045] According to an embodiment of the present invention, determining the device healthy operation coefficient of the scheduled device according to the device status information includes: Determining the device real-time power, device temperature, and device signal strength according to the device status information; Determining the device battery life identification result according to the device real-time power; Determining the device temperature identification result according to the device temperature; Determining the device signal identification result according to the device signal strength; Determining the device healthy operation coefficient of the scheduled device according to the device battery life identification result, the device temperature identification result, and the device signal identification result.

[0046] For example, obtain the real-time power of the device through the battery management chip in the unmanned device, obtain the device temperature inside the device through the integrated temperature sensor (such as, DHT11) in the unmanned device, and obtain the device signal strength of the unmanned device through the AT command of the communication module in the unmanned device; according to the real-time power of the device, evaluate whether the power of the device can support the completion of the task, and determine the device endurance recognition result; according to the device temperature, evaluate the real-time working condition of the device. An abnormally high device temperature may indicate that the device has a fault, and determine the device temperature recognition result; according to the device signal strength, evaluate the communication condition of the unmanned device during operation. If the communication condition is poor, it will affect the response speed and working accuracy of the unmanned device, and determine the device signal recognition result. If the device signal strength is less than the preset device signal strength threshold, the device signal recognition result is -1. If the signal strength is greater than or equal to the preset device signal strength threshold, the device signal recognition result is 1; according to the sum of the device endurance recognition result, the device temperature recognition result, and the device signal recognition result, determine the device healthy operation coefficient.

[0047] According to an embodiment of the present invention, determining the device endurance recognition result according to the real-time power of the device includes: Obtain the initial power of the unmanned device; According to the initial power and the real-time power of the device, determine the consumed power; Determine the start scheduling time of the unmanned device; According to the start scheduling time, determine the scheduled time; According to the consumed power and the scheduled time, determine the actual power consumption speed; According to the expected task duration and the initial power, determine the expected power consumption speed; According to the actual power consumption speed and the expected power consumption speed, determine the power consumption recognition result; According to the real-time power of the device and the preset device power threshold, determine the device power recognition result; According to the power consumption recognition result and the device power recognition result, determine the device endurance recognition result.

[0048] For example, determine the initial power of the device when it starts to execute a task; determine the consumed power by subtracting the real-time power of the device from the initial power; determine the last start scheduling time of the unmanned device according to the system log of the unmanned device; determine the scheduled time by subtracting the start scheduling time from the current time; determine the actual power consumption speed according to the ratio of the consumed power to the scheduled time; determine the expected power consumption speed according to the ratio of the initial power to the actual duration of the expected task; if the actual power consumption speed is greater than the expected power consumption speed, the power consumption identification result is -1, if the actual power consumption speed is less than or equal to the expected power consumption speed, the power consumption identification result is 1; the preset device power threshold can be set to 30%, if the real-time power of the device is less than or equal to the preset device power threshold, the device power identification result is -1, if the real-time power of the device is greater than the preset device power threshold, the device power identification result is 1; if the sum of the power consumption identification result and the device power identification result is -2, the device battery life identification result is -1, when the sum of the power consumption identification result and the device power identification result is greater than -2, the device battery life identification result is 1, that is, only when the device's power consumption is too fast and the device's power is too low, the device battery life identification result is -1.

[0049] According to an embodiment of the present invention, determining the device temperature identification result according to the device temperature includes: Fitting the device temperature and the moment in the scheduling period to obtain a device temperature function of the device temperature in the scheduling period; Determine the device temperature derivative function according to the device temperature function; Determine the device temperature change rate at multiple moments in the scheduling period according to the device temperature derivative function; Determine the device temperature identification result according to the device temperature and the device temperature change rate.

[0050] For example, fit the device temperature and the moment in the scheduling period to obtain a device temperature function that describes the law of the device temperature changing over time in the current detection period described by the user; take the derivative of the device temperature function to determine the device temperature derivative function; substitute multiple moments in the scheduling period into the device temperature derivative function to determine the device temperature change rate at multiple moments in the scheduling period; evaluate the temperature condition of the device according to the device temperature and the device temperature change rate, and determine the device temperature identification result.

[0051] According to an embodiment of the present invention, determining the device temperature identification result according to the device temperature and the device temperature change rate includes: Obtain the ambient temperature at multiple moments in the scheduling period according to the temperature sensor set on the unmanned device; Determine the preset temperature threshold according to the ambient temperature; Determine a first temperature recognition result according to the preset temperature threshold and the device temperature; Determine a second temperature recognition result according to the device temperature change rate and a preset device temperature change rate threshold; Determine a device temperature recognition result according to the first temperature recognition result and the second temperature recognition result.

[0052] For example, obtain the ambient temperature of the working environment of the unmanned device through a temperature sensor in the unmanned device; determine the preset temperature threshold according to the ambient temperature. For example, when the ambient temperature is between 5 degrees Celsius and 30 degrees Celsius, the preset temperature threshold is the ambient temperature plus 15 degrees Celsius; when the ambient temperature is between 30 degrees Celsius and 50 degrees Celsius, the preset temperature threshold is 45 degrees Celsius; when the ambient temperature is greater than 50 degrees Celsius, the preset temperature threshold is 55 degrees Celsius; when the device temperature is greater than the preset temperature threshold, it indicates that the temperature of the unmanned device is too high and there may be a fault, and the first temperature recognition result is -1; when the device temperature is less than or equal to the preset temperature threshold, it indicates that the temperature of the unmanned device is normal, and the first temperature recognition result is 1; when the device temperature change rate is greater than the preset device temperature change rate threshold, it indicates that the temperature rise of the unmanned device is abnormal and there may be a fault, and the second temperature recognition result is -1; when the device temperature change rate is less than or equal to the preset device temperature change rate threshold, it indicates that there is no abnormal temperature rise in the unmanned device, and the second temperature recognition result is 1. The preset device temperature change rate threshold can be set to 2 degrees Celsius per minute; if the sum of the first temperature recognition result and the second temperature recognition result is less than 2, the device temperature recognition result is -1; if the sum of the first temperature recognition result and the second temperature recognition result is equal to 2, the device temperature recognition result is 1, indicating that when there is a situation of too large a temperature change rate or too high a device temperature, the device temperature recognition result is -1, and when both the temperature change rate and the device temperature are within the normal range, the device temperature recognition result is 1.

[0053] According to an embodiment of the present invention, in the fault recovery module, it is used to determine a fault recovery scheduling result according to the device healthy operation coefficient.

[0054] For example, if the device healthy operation coefficient is less than 3, it indicates that there may be a fault in the unmanned device, then the fault recovery scheduling result is 1, and fault recovery scheduling is required; if the device healthy operation coefficient is equal to 3, it indicates that there is no fault in the unmanned device, then the fault recovery scheduling result is 0, indicating that no fault recovery scheduling is required.

[0055] According to an embodiment of the present invention, in the scheduling policy module, it is used to determine a scheduling policy according to the priority scheduling task and the fault recovery scheduling result.

[0056] For example, at each moment in the scheduling cycle, the priority-scheduled tasks are scheduled, and the unmanned devices executing the tasks are monitored in real time. When the fault recovery scheduling result of the unmanned device is 1, it indicates that the unmanned device has a fault. Among the unscheduled unmanned devices, select a device with the same or similar payload and power as the unmanned device with a fault recovery scheduling result of 1, and make the selected unmanned device go to the location where the unmanned device may occur to help continue executing the task.

[0057] The unmanned aerial vehicle cluster control system according to the embodiment of the present invention can accurately obtain the task information and the device information of the unmanned devices, and determine the device requirement plan for each task according to the device information and the task information. Further, it can analyze the resource utilization status of the device requirement plan for each task, and determine the scheduling order of each task according to the resource utilization status and the task information. On the other hand, during the scheduling process, it can monitor and analyze the healthy working status of the unmanned devices in real time, and determine whether it is necessary to perform fault recovery scheduling on the unmanned devices according to the healthy working status, improving the utilization rate of unmanned device resources and the completion rate of tasks. When determining the task priority scheduling coefficient, the task priority scheduling coefficient can be determined according to the task required payload, task required power, task priority, expected task duration, total schedulable payload, total schedulable power, payload utilization coefficient, and power utilization coefficient. During the calculation process, the task priority scheduling coefficient can be determined according to three aspects: the preset priority status, resource usage status, and resource overload status, improving the utilization rate of unmanned device resources while meeting the urgent needs of the tasks, and reducing the occurrence of deadlock phenomena of unmanned device resources, improving the comprehensiveness and accuracy of the task priority scheduling coefficient.

[0058] Figure 2 Exemplarily, a schematic flowchart of the unmanned aerial vehicle cluster control method according to the embodiment of the present invention is shown, and the method includes: Step S1, at multiple moments in the scheduling cycle, obtain the task information of the task, where the task information includes: task basic information and task requirement information; Step S2, at multiple moments in the scheduling cycle, obtain the device information of the unmanned device, where the device information includes: device status information and device function information; Step S3, determine the task requirement device plan according to the task requirement information and the device information; Step S4, determine the payload utilization coefficient and the power utilization coefficient of the task requirement device plan; Step S5, determine the task priority scheduling coefficient according to the task basic information, the payload utilization coefficient, and the power utilization coefficient; Step S6, determine the priority-scheduled task according to the task priority scheduling coefficient; Step S7: Determine the device healthy operation coefficient of the scheduled devices according to the device status information; Step S8: Determine the fault recovery scheduling result according to the device healthy operation coefficient; Step S9: Determine the scheduling strategy according to the priority scheduling tasks and the fault recovery scheduling result.

[0059] The present invention can be a method, a device, a system, and / or a computer program product. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for performing various aspects of the present invention.

[0060] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been shown and described in the embodiments. Without departing from the principle, the embodiments of the present invention can have any deformation or modification.

Claims

1. An unmanned aerial vehicle cluster control system, characterized in that, Including: A task information module, configured to obtain task information of a task at multiple moments within a scheduling period, where the task information includes: task basic information and task requirement information; A device information module, configured to obtain device information of an unmanned device at multiple moments within a scheduling period, where the device information includes: device status information and device function information; A device solution module, configured to determine a task requirement device solution according to the task requirement information and the device information; A utilization coefficient module, configured to determine a load utilization coefficient and a power utilization coefficient of the task requirement device solution; A scheduling coefficient module, configured to determine a task priority scheduling coefficient according to the task basic information, the load utilization coefficient, and the power utilization coefficient; A scheduling order module, configured to determine a prioritized scheduling task according to the task priority scheduling coefficient; An operation coefficient module, configured to determine a device healthy operation coefficient of a scheduled device according to the device status information; A fault recovery module, configured to determine a fault recovery scheduling result according to the device healthy operation coefficient; A scheduling strategy module, configured to determine a scheduling strategy according to the prioritized scheduling task and the fault recovery scheduling result.

2. The drone swarm control system according to claim 1, characterized in that Determining a task requirement device solution according to the task requirement information and the device information includes: Determining a task requirement load amount and a task requirement power according to the task requirement information; Determining a device idle state according to the device status information; Determining schedulable devices according to the device idle state; Determining a schedulable unmanned device load amount and a schedulable unmanned device power of multiple schedulable devices according to the device function information; Determining a task requirement device solution according to the task requirement load amount, the task requirement power, the schedulable unmanned device load amount, and the schedulable unmanned device power.

3. The drone swarm control system according to claim 2, wherein Determining a load utilization coefficient and a power utilization coefficient of the task requirement device solution includes: Determining an expected device load amount and an expected device power of the task requirement device solution; Determining a load utilization coefficient of the task requirement device solution according to the expected device load amount and the task requirement load amount; Determining a power utilization coefficient of the task requirement device solution according to the expected device power and the task requirement power.

4. The drone swarm control system according to claim 3, wherein, Determining a task priority scheduling coefficient according to the task basic information, the load utilization coefficient, and the power utilization coefficient includes: Determining a total schedulable load amount and a total schedulable power according to the schedulable unmanned device load amount and the schedulable unmanned device power; Determining an expected task duration according to the task basic information; Determining a task priority according to the task basic information; Determining a task priority scheduling coefficient according to the task requirement load amount, the task requirement power, the task priority, the expected task duration, the total schedulable load amount, the total schedulable power, the load utilization coefficient, and the power utilization coefficient.

5. The UAV swarm control system according to claim 4, wherein Determine the task priority scheduling coefficient according to the task required payload, the task required power, the task priority, the expected task duration, the total schedulable payload, the total schedulable power, the payload utilization factor, and the power utilization factor, including: According to the formula Determine the task priority scheduling coefficient of the k-th task at the i-th moment of the scheduling period , where and are preset weights, if is a conditional function, and or is the logical operator "or". is the task priority of the k-th task, is the load utilization coefficient of the k-th task at the i-th moment of the scheduling period, is the power utilization coefficient of the k-th task at the i-th moment of the scheduling period, is the task required load of the k-th task, is the task required power of the k-th task, is the expected task duration of the k-th task, is the total schedulable load at the i-th moment of the scheduling period, is the total schedulable power at the i-th moment of the scheduling period, and K is the number of tasks at the i-th moment of the scheduling period. k ≤ K, and both k and K are positive integers.

6. The drone swarm control system according to claim 4, wherein Determine the equipment healthy operation coefficient of the scheduled equipment according to the equipment status information, including: Determine the real-time power of the equipment, the equipment temperature, and the equipment signal strength according to the equipment status information; Determine the equipment battery life identification result according to the real-time power of the equipment; Determine the equipment temperature identification result according to the equipment temperature; Determine the equipment signal identification result according to the equipment signal strength; Determine the equipment healthy operation coefficient of the scheduled equipment according to the equipment battery life identification result, the equipment temperature identification result, and the equipment signal identification result.

7. The UAV swarm control system according to claim 6, characterized in that, Determine the equipment battery life identification result according to the real-time power of the equipment, including: Obtain the initial power of the unmanned equipment; Determine the consumed power according to the initial power and the real-time power of the equipment; Determine the start scheduling time of the unmanned equipment; Determine the scheduled time according to the start scheduling time; Determine the actual power consumption speed according to the consumed power and the scheduled time; Determine the expected power consumption speed according to the expected task duration and the initial power; Determine the power consumption identification result according to the actual power consumption speed and the expected power consumption speed; Determine the equipment power identification result according to the real-time power of the equipment and the preset equipment power threshold; Determine the equipment battery life identification result according to the power consumption identification result and the equipment power identification result.

8. The UAV swarm control system according to claim 6, characterized in that, Determine the equipment temperature identification result according to the equipment temperature, including: Fit according to the equipment temperature and the moment in the scheduling period to obtain the equipment temperature function of the equipment temperature in the scheduling period; Determine the equipment temperature derivative function according to the equipment temperature function; Determine the equipment temperature change rate at multiple moments in the scheduling period according to the equipment temperature derivative function; Determine the equipment temperature identification result according to the equipment temperature and the equipment temperature change rate.

9. The drone swarm control system according to claim 8, wherein Determine the equipment temperature identification result according to the equipment temperature and the equipment temperature change rate, including: Obtain the ambient temperature at multiple moments in the scheduling period according to the temperature sensor set on the unmanned equipment; Determine the preset temperature threshold according to the ambient temperature; Determine the first temperature identification result according to the preset temperature threshold and the equipment temperature; Determine the second temperature identification result according to the equipment temperature change rate and the preset equipment temperature change rate threshold; Determine the equipment temperature identification result according to the first temperature identification result and the second temperature identification result.

10. A method for controlling a drone swarm, characterized in that, Include: Obtain the task information of the task at multiple moments in the scheduling period, where the task information includes: task basic information and task requirement information; Obtain the equipment information of the unmanned equipment at multiple moments in the scheduling period, where the equipment information includes: equipment status information and equipment function information; Determine the task requirement equipment plan according to the task requirement information and the equipment information. Determine the load utilization factor and power utilization factor of the task requirement device solution; Determine the task priority scheduling factor according to the task basic information, the load utilization factor and the power utilization factor; Determine the priority scheduling tasks according to the task priority scheduling factor; Determine the device healthy operation factor of the scheduled devices according to the device status information; Determine the fault recovery scheduling result according to the device healthy operation factor; Determine the scheduling strategy according to the priority scheduling tasks and the fault recovery scheduling result.

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