UAV swarm control system

Abstract

The present invention provides a drone cluster control system, which relates to the field of drones. The system includes: a task information module for obtaining task information; an equipment information module for obtaining equipment information of unmanned equipment; an equipment solution module for determining the equipment solution required by the task; a utilization coefficient module for determining the load utilization coefficient and power utilization coefficient of the equipment solution required by the task; a scheduling coefficient module for determining the task priority scheduling coefficient based on the basic task information, the load utilization coefficient, and the power utilization coefficient; a scheduling sequence module for determining the priority scheduling task; an operation coefficient module for determining the equipment health operation coefficient of the scheduled equipment; a fault recovery module for determining the fault recovery scheduling result based on the equipment health operation coefficient; and a scheduling strategy module for determining the scheduling strategy. According to the present invention, the utilization rate of unmanned equipment resources and the task completion rate can be improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicles (UAVs), and in particular to a UAV cluster control system. Background Art

[0002] In related technologies, the scheduling of drone clusters mainly relies on manual scheduling, that is, it mainly relies on human factors. Excessive reliance on human factors may make it difficult to accurately match the capabilities of unmanned equipment, resulting in the failure of unmanned equipment to fully exert its efficiency, resulting in high energy consumption and insufficient fault recovery mechanism.

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

[0004] The present invention provides a drone cluster control system that can solve the technical problems that related technologies cannot accurately match the capabilities of unmanned equipment, resulting in the unmanned equipment failing to fully exert its efficiency, causing high energy consumption and insufficient fault recovery mechanism.

[0005] According to a first aspect of the present invention, there is provided a drone cluster control system, comprising:

[0006] The task information module is used to obtain task information of the task at multiple times in the scheduling cycle, wherein the task information includes: basic task information and task requirement information;

[0007] The device information module is used to obtain device information of unmanned devices at multiple times during the scheduling cycle, wherein the device information includes: device status information and device function information;

[0008] An equipment solution module, configured to determine a task requirement equipment solution based on the task requirement information and the equipment information;

[0009] A utilization coefficient module, used to determine the load utilization coefficient and power utilization coefficient of the equipment solution required by the mission;

[0010] A scheduling coefficient module, configured to determine a task priority scheduling coefficient based on the task basic information, the load utilization coefficient, and the power utilization coefficient;

[0011] A scheduling sequence module, used for determining priority scheduling tasks according to the task priority scheduling coefficient;

[0012] An operation coefficient module, configured to determine a device health operation coefficient of a scheduled device based on the device status information;

[0013] A fault recovery module, configured to determine a fault recovery scheduling result based on the health operation coefficient of the equipment;

[0014] The scheduling strategy module is used to determine the scheduling strategy according to the priority scheduling task and the fault recovery scheduling result.

[0015] According to the present invention, determining a task requirement equipment solution based on the task requirement information and the equipment information includes:

[0016] Determining the mission required load and mission required power according to the mission required information;

[0017] Determining a device idle state according to the device state information;

[0018] Determining a schedulable device according to the idle state of the device;

[0019] Determining, based on the device function information, the dispatchable unmanned device load and the dispatchable unmanned device power of the plurality of dispatchable devices;

[0020] The mission required equipment plan is determined based on the mission required load, the mission required power, the schedulable unmanned equipment load and the schedulable unmanned equipment power.

[0021] According to the present invention, determining the load utilization coefficient and the power utilization coefficient of the mission required equipment solution includes:

[0022] Determine the expected equipment load and expected equipment power of the mission required equipment solution;

[0023] Determining a load utilization coefficient of a mission-required equipment solution based on the expected equipment load and the mission-required load;

[0024] The power utilization coefficient of the mission required equipment solution is determined based on the expected equipment power and the mission required power.

[0025] According to the present invention, determining a task priority scheduling coefficient based on the task basic information, the load utilization coefficient, and the power utilization coefficient includes:

[0026] Determining a total schedulable load and a total schedulable power according to the schedulable unmanned equipment load and the schedulable unmanned equipment power;

[0027] Determine the expected task duration based on the basic task information;

[0028] Determine the task priority based on the basic task information;

[0029] A task priority scheduling coefficient is determined 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.

[0030] According to 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:

[0031] According to the formula

[0032]

[0033] Determine the task priority scheduling coefficient of the kth task at the i-th moment of the scheduling cycle ,in, and is the preset weight, if is the conditional function, or is the logical operator of "or", is the task priority of the kth task, is the load utilization coefficient of the kth task at the i-th moment of the scheduling cycle, is the power utilization coefficient of the kth task at the i-th moment of the scheduling period, is the task load requirement of the kth task, is the task power requirement of the kth task, is the expected task duration of the k-th task, is the total dispatchable load at the i-th moment of the dispatch 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.

[0034] According to the present invention, determining the equipment health coefficient of the scheduled equipment based on the equipment status information includes:

[0035] Determine the device's real-time power level, device temperature, and device signal strength based on the device status information;

[0036] Determine the device battery life identification result based on the real-time power of the device;

[0037] Determining a device temperature identification result according to the device temperature;

[0038] Determining a device signal recognition result according to the device signal strength;

[0039] The device health coefficient of the scheduled device is determined according to the device life identification result, the device temperature identification result, and the device signal identification result.

[0040] According to the present invention, determining the device battery life identification result based on the real-time power level of the device includes:

[0041] Get the initial power of the unmanned device;

[0042] Determining the consumed power based on the initial power and the real-time power of the device;

[0043] Determine the start time of dispatching unmanned equipment;

[0044] Determining the scheduled time according to the start scheduling time;

[0045] determining an actual power consumption rate based on the consumed power and the scheduled time;

[0046] determining an expected power consumption rate based on the expected task duration and the initial power;

[0047] determining a power consumption identification result according to the actual power consumption rate and the expected power consumption rate;

[0048] Determine a device power identification result based on the real-time power of the device and a preset device power threshold;

[0049] Determine a device battery life identification result based on the power consumption identification result and the device power identification result.

[0050] According to the present invention, determining a device temperature identification result according to the device temperature includes:

[0051] Performing fitting based on the device temperature and the time in the scheduling period to obtain a device temperature function of the device temperature in the scheduling period;

[0052] determining a device temperature derivative function based on the device temperature function;

[0053] Determining the device temperature change rate at multiple moments in the scheduling period based on the device temperature derivative function;

[0054] A device temperature identification result is determined according to the device temperature and the device temperature change rate.

[0055] According to the present invention, determining a device temperature identification result according to the device temperature and the device temperature change rate includes:

[0056] Obtain the ambient temperature at multiple times during the scheduling cycle using the temperature sensor installed on the unmanned equipment;

[0057] Determining a preset temperature threshold according to the ambient temperature;

[0058] Determining a first temperature recognition result according to the preset temperature threshold and the device temperature;

[0059] determining a second temperature identification result according to the device temperature change rate and a preset device temperature change rate threshold;

[0060] A device temperature recognition result is determined according to the first temperature recognition result and the second temperature recognition result.

[0061] According to a second aspect of the present invention, a method for controlling a drone cluster is provided, comprising:

[0062] At multiple moments in the scheduling cycle, task information of the task is obtained, wherein the task information includes: basic task information and task requirement information;

[0063] At multiple moments in the scheduling cycle, device information of the unmanned device is obtained, wherein the device information includes: device status information and device function information;

[0064] Determining a task requirement equipment solution based on the task requirement information and the equipment information;

[0065] Determine the load utilization factor and power utilization factor of the equipment solution required by the mission;

[0066] Determining a task priority scheduling coefficient based on the task basic information, the load utilization coefficient, and the power utilization coefficient;

[0067] Determining the priority scheduling tasks according to the task priority scheduling coefficient;

[0068] Determining the equipment health coefficient of the scheduled equipment based on the equipment status information;

[0069] Determining a fault recovery scheduling result based on the equipment health operation coefficient;

[0070] A scheduling strategy is determined according to the priority scheduling task and the fault recovery scheduling result.

[0071] Technical effect: According to the present invention, task information and equipment information of unmanned equipment can be accurately obtained, and the equipment demand plan of each task can be determined based on the equipment information and task information. Furthermore, the resource utilization status of the equipment demand plan of each task can be analyzed, and the scheduling order of each task can be determined based on the resource utilization status and task information. On the other hand, the healthy working status of the unmanned equipment can be monitored and analyzed in real time during the scheduling process, and whether recovery scheduling is required for the failure of the unmanned equipment can be determined based on the healthy working status, thereby improving the utilization rate of unmanned equipment resources and the completion rate of tasks. When determining the task priority scheduling coefficient, the task priority scheduling coefficient can be determined based on the task required load, task required 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 based on the three aspects of pre-set priority status, resource usage status and resource overload status. While meeting the urgent needs of the task, the utilization rate of unmanned equipment resources is improved, and the occurrence of unmanned equipment resource deadlock is reduced, thereby improving the comprehensiveness and accuracy of the task priority scheduling coefficient.

[0072] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and not limiting of the present invention. Other features and aspects of the present invention will become more apparent from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can derive other embodiments based on these drawings without inventive efforts.

[0074] Figure 1 The following is a block diagram of a UAV cluster control system according to an embodiment of the present invention;

[0075] Figure 2 The following is a flow chart showing an exemplary method for controlling a drone cluster according to an embodiment of the present invention. DETAILED DESCRIPTION

[0076] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. 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 shall fall within the scope of protection of the present invention.

[0077] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0078] Figure 1 A block diagram of a drone cluster control system according to an embodiment of the present invention is exemplarily shown, wherein the system includes:

[0079] The task information module is used to obtain task information of the task at multiple times in the scheduling cycle, wherein the task information includes: basic task information and task requirement information;

[0080] The device information module is used to obtain device information of unmanned devices at multiple times during the scheduling cycle, wherein the device information includes: device status information and device function information;

[0081] An equipment solution module, configured to determine a task requirement equipment solution based on the task requirement information and the equipment information;

[0082] A utilization coefficient module, used to determine the load utilization coefficient and power utilization coefficient of the equipment solution required by the mission;

[0083] A scheduling coefficient module, configured to determine a task priority scheduling coefficient based on the task basic information, the load utilization coefficient, and the power utilization coefficient;

[0084] A scheduling sequence module, used for determining priority scheduling tasks according to the task priority scheduling coefficient;

[0085] An operation coefficient module, configured to determine a device health operation coefficient of a scheduled device based on the device status information;

[0086] A fault recovery module, configured to determine a fault recovery scheduling result based on the health operation coefficient of the equipment;

[0087] The scheduling strategy module is used to determine the scheduling strategy according to the priority scheduling task and the fault recovery scheduling result.

[0088] According to the drone cluster control system of an embodiment of the present invention, the mission information and equipment information of the unmanned equipment can be accurately obtained, and the equipment requirement plan for each task can be determined based on the equipment information and mission information. Furthermore, the resource utilization status of the equipment requirement plan for each task can be analyzed, and the scheduling order of each task can be determined based on the resource utilization status and mission information. On the other hand, during the scheduling process, the healthy working status of the unmanned equipment can be monitored and analyzed in real time, and based on the healthy working status, it can be determined whether recovery scheduling is required for the failure of the unmanned equipment, thereby improving the utilization rate of unmanned equipment resources and the task completion rate.

[0089] According to one embodiment of the present invention, a task information module is configured to obtain task information for a task at multiple moments in a scheduling cycle, where the task information includes basic task information and task requirement information. For example, a scheduling cycle refers to the time period for scheduling and executing a task, such as a 24-hour period, with a five-minute interval between two adjacent moments in the scheduling cycle.

[0090] For example, in the task information module, the task content of the task can be recorded when the task is released, and the basic task information (such as expected task duration, task priority) and task requirement information (requirements that need to be met during task execution, such as task required load) of the task can be determined based on the task content.

[0091] According to an embodiment of the present invention, in the device information module, it is used to obtain device information of unmanned devices at multiple moments in the scheduling cycle, wherein the device information includes: device status information and device function information.

[0092] For example, based on the sensors installed in unmanned equipment (such as drones), the equipment status information (such as the real-time power level and temperature of the equipment) can be obtained in real time, and the equipment function information (such as the load capacity and power of the unmanned equipment) can be determined based on the factory information of the unmanned equipment.

[0093] According to one embodiment of the present invention, in the equipment solution module, it is used to determine the task requirement equipment solution according to the task requirement information and the equipment information.

[0094] According to one embodiment of the present invention, determining a task requirement equipment solution based on the task requirement information and the equipment information includes:

[0095] Determining the mission required load and mission required power according to the mission required information;

[0096] Determining a device idle state according to the device state information;

[0097] Determining a schedulable device according to the idle state of the device;

[0098] Determining, based on the device function information, the dispatchable unmanned device load and the dispatchable unmanned device power of the plurality of dispatchable devices;

[0099] The mission required equipment plan is determined based on the mission required load, the mission required power, the schedulable unmanned equipment load and the schedulable unmanned equipment power.

[0100] For example, according to the task requirement information, the task requirement load and task requirement power are determined. For example, if task B needs to transport 1000kg of objects and requires a 50kW UAV to perform a stable inspection task in a harsh environment (such as a typhoon or blizzard), the task requirement load and task requirement power of task B are 1000kg and 50kW respectively. According to the equipment status information, the equipment idle state is determined, and the equipment idle state includes "idle" and "scheduling". The unmanned equipment in the "idle" state is determined as a schedulable equipment. According to the equipment function information of the schedulable equipment, the schedulable unmanned equipment load and schedulable unmanned equipment are determined. Backup power; determine the mission requirement equipment plan based on the mission required payload, mission required power, dispatchable unmanned equipment payload and dispatchable unmanned equipment power. For example, the mission required payload of mission A is 1000kg. When there is a dispatchable device with a dispatchable unmanned equipment payload of 1000kg, a drone with a payload of 1000kg can be assigned to the mission. When there is no dispatchable device with a dispatchable unmanned equipment payload of 1000kg, then among the dispatchable unmanned equipment, select the drone with the payload closest to the mission requirement and greater than the mission requirement. For example, a drone with a payload of 1100kg can be assigned to the mission.

[0101] According to one embodiment of the present invention, in the utilization coefficient module, it is used to determine the load utilization coefficient and the power utilization coefficient of the mission required equipment solution.

[0102] According to one embodiment of the present invention, determining the load utilization coefficient and the power utilization coefficient of the mission required equipment solution includes:

[0103] Determine the expected equipment load and expected equipment power of the mission required equipment solution;

[0104] Determining a load utilization coefficient of a mission-required equipment solution based on the expected equipment load and the mission-required load;

[0105] The power utilization coefficient of the mission required equipment solution is determined based on the expected equipment power and the mission required power.

[0106] For example, determine the expected equipment load and expected equipment power of the mission-required equipment plan. For example, according to the mission-required equipment plan, a drone with a load capacity of 1100kg and a drone with a power of 60kw for stable inspection tasks in harsh environments are assigned to mission A. Then the expected equipment load and expected equipment power of the mission-required equipment plan are 1100kg and 60kw respectively, and the mission-required load and mission-required power of the mission are 1000kg and 50kw; determine the load utilization coefficient of the mission-required equipment plan based on the ratio of the mission-required load and the expected equipment load; determine the power utilization coefficient of the mission-required equipment plan based on the ratio of the mission-required power and the expected equipment power.

[0107] According to one 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 coefficient and the power utilization coefficient.

[0108] According to one embodiment of the present invention, determining a task priority scheduling coefficient based on the task basic information, the load utilization coefficient, and the power utilization coefficient includes:

[0109] Determining a total schedulable load and a total schedulable power according to the schedulable unmanned equipment load and the schedulable unmanned equipment power;

[0110] Determine the expected task duration based on the basic task information;

[0111] Determine the task priority based on the basic task information;

[0112] A task priority scheduling coefficient is determined 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.

[0113] For example, the total schedulable load and total schedulable power are determined based on the sum of the schedulable unmanned equipment load and the schedulable unmanned equipment power of each schedulable equipment; based on the basic information of the task, determine how long it is expected to take to complete the task, that is, the expected task duration; based on the basic information of the task, determine the task priority of the task, and when the task is released, the priority of the task scheduling is graded. The task priority is an integer from 1 to 10. The larger the task priority, the higher the priority of the task scheduling; based on the task required load, task required power, task priority, expected task duration, the total schedulable load, total schedulable power, load utilization coefficient and power utilization coefficient, the priority status of task scheduling is further analyzed and evaluated to determine the task priority scheduling coefficient.

[0114] According to one embodiment of the present invention, the task priority scheduling coefficient is determined based on 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, including: determining the task priority scheduling coefficient of the kth task at the i-th moment of the scheduling period according to formula (1): ,

[0115] (1)

[0116] in, and is the preset weight, if is the conditional function, or is the logical operator of "or", is the task priority of the kth task, is the load utilization coefficient of the kth task at the i-th moment of the scheduling cycle, is the power utilization coefficient of the kth task at the i-th moment of the scheduling period, is the task load requirement of the kth task, is the task power requirement of the kth task, is the expected task duration of the k-th task, is the total dispatchable load at the i-th moment of the dispatch 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.

[0117] According to one embodiment of the present invention, It is the relative difference between the task priority of the k-th task and the average task priority of K tasks. The larger the ratio is, the higher the task priority of the k-th task is.

[0118] According to one embodiment of the present invention, is the ratio of the task load requirement of the kth task to the average task load requirement of K tasks. The larger the ratio is, the larger the task load requirement of the kth task is. It is the ratio of the task power requirement of the kth task to the average task power requirement of K tasks. The larger the ratio is, the larger the task power requirement of the kth task is. It is the ratio of the expected task duration of the kth task to the average expected task duration of K tasks. The larger the ratio is, the longer the expected task duration of the kth task is and the longer the resource occupation time is. To determine the resource usage of the kth task based on the task load and task power requirements of the kth task, To determine the resource occupancy level of the kth task based on the resource occupancy and resource occupancy duration of the kth task, is the sum of the load utilization coefficient and power utilization coefficient of the kth task at the i-th moment of the scheduling cycle, indicating the resource utilization degree of the kth task at the i-th moment of the scheduling cycle. It is the ratio of the sum of the resource utilization degree of the kth task at the i-th moment of the scheduling cycle and the resource occupancy degree of the kth task to the resource occupancy degree of the kth task. The larger the ratio, the higher the resource utilization degree of the kth task at the i-th moment of the scheduling cycle. Unmanned equipment resources are allocated to the task first, and the resulting resource waste is smaller. The larger the ratio, the lower the resource occupancy degree of the kth task. Unmanned equipment resources are allocated to the task first, so that the unmanned equipment resources can be fully utilized, which can avoid the situation where a task occupies a large amount of unmanned equipment resources for a long time, resulting in the inability to execute other tasks.

[0119] According to one embodiment of the present invention, in formula (1), the conditional function The value of includes the following two cases, when satisfying When the condition is met, it means that the task load exceeds the total schedulable load or the task power exceeds the total schedulable power. The value of the condition function is -10 , in dissatisfaction When the condition is met, the value of the condition function is 0.

[0120] According to one embodiment of the present invention, Indicates that the task priority scheduling coefficient is determined based on the pre-set priority status, resource usage status, and resource overload status. Much greater than , and they are all positive integers, indicating that when determining the task priority scheduling coefficient, the priority of the task is given priority, and resources are scheduled first for tasks with higher priority to meet the urgent needs of the task. When the task priorities are the same, the resource usage of the task is considered, and resources are scheduled first for tasks with lower resource occupancy and higher resource utilization. Can be set to 1000, Can be set to 1. When resource overload occurs, the condition function The value is -10 , since the maximum value of the task priority is 10, Less than 10 ,and Much greater than , when a resource overload occurs, the value of the task priority scheduling coefficient is much less than 0, avoiding the unmanned equipment resource deadlock phenomenon caused by when a task with too high priority requires too many unmanned equipment resources and the schedulable resources cannot meet the task requirements.

[0121] In this way, the task priority scheduling coefficient can be determined based on the task required load, task required 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 based on the three aspects of pre-set priority status, resource usage status and resource overload status. While meeting the urgent needs of the task, the utilization rate of unmanned equipment resources is improved, and the occurrence of unmanned equipment resource deadlock is reduced, thereby improving the comprehensiveness and accuracy of the task priority scheduling coefficient.

[0122] According to an embodiment of the present invention, in the scheduling sequence module, it is used to determine the priority scheduling task according to the task priority scheduling coefficient.

[0123] For example, the task corresponding to the maximum value of the task priority scheduling coefficient of all tasks is determined as the priority scheduling task.

[0124] According to one embodiment of the present invention, in the operation coefficient module, it is used to determine the equipment health operation coefficient of the scheduled equipment according to the equipment status information.

[0125] According to one embodiment of the present invention, determining the device health coefficient of the scheduled device based on the device status information includes:

[0126] Determine the device's real-time power level, device temperature, and device signal strength based on the device status information;

[0127] Determine the device battery life identification result based on the real-time power of the device;

[0128] Determining a device temperature identification result according to the device temperature;

[0129] Determining a device signal recognition result according to the device signal strength;

[0130] The device health coefficient of the scheduled device is determined according to the device life identification result, the device temperature identification result, and the device signal identification result.

[0131] For example, the real-time power level of the device is obtained through the battery management chip in the unmanned device, the internal device temperature of the device is obtained through the integrated temperature sensor (such as DHT11) in the unmanned device, and the device signal strength of the unmanned device is obtained through the AT command of the communication module in the unmanned device; based on the real-time power level of the device, evaluate whether the power level of the device can support the completion of the task, and determine the device endurance identification result; based on the device temperature, evaluate the real-time working status of the device. Abnormally high temperature of the device may indicate a device failure, and determine the device temperature identification result; based on the device signal strength, evaluate the communication status of the unmanned device during operation. If the communication status is poor, it will affect the response speed and working accuracy of the unmanned device, and determine the device signal identification result. If the device signal strength is less than the preset device signal strength threshold, the device signal identification result is -1. If the signal strength is greater than or equal to the preset device signal strength threshold, the device signal identification result is 1; based on the sum of the device endurance identification result, the device temperature identification result and the device signal identification result, determine the device health operation coefficient.

[0132] According to one embodiment of the present invention, determining a device battery life identification result based on the real-time power level of the device includes:

[0133] Get the initial power of the unmanned device;

[0134] Determining the consumed power based on the initial power and the real-time power of the device;

[0135] Determine the start time of dispatching unmanned equipment;

[0136] Determining the scheduled time according to the start scheduling time;

[0137] determining an actual power consumption rate based on the consumed power and the scheduled time;

[0138] determining an expected power consumption rate based on the expected task duration and the initial power;

[0139] determining a power consumption identification result according to the actual power consumption rate and the expected power consumption rate;

[0140] Determine a device power identification result based on the real-time power of the device and a preset device power threshold;

[0141] Determine a device battery life identification result based on the power consumption identification result and the device power identification result.

[0142] For example, determine the initial power of the device when it starts to perform a task; determine the consumed power based on the initial power minus the real-time power of the device; determine the most recent start scheduling time of the unmanned device based on the system log of the unmanned device; determine the scheduled time based on the current time minus the start scheduling time; determine the actual power consumption rate based on the ratio of consumed power to scheduled time; determine the expected power consumption rate based on the ratio of the initial power and the expected task duration; if the actual power consumption rate is greater than the expected power consumption rate, the power consumption identification result is -1; if the actual power consumption rate is less than or equal to the expected power consumption rate, the power consumption identification result is -1. 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 power is consumed too quickly and the device power is too low, the device battery life identification result is -1.

[0143] According to one embodiment of the present invention, determining a device temperature identification result according to the device temperature includes:

[0144] Performing fitting based on the device temperature and the time in the scheduling period to obtain a device temperature function of the device temperature in the scheduling period;

[0145] determining a device temperature derivative function based on the device temperature function;

[0146] Determining the device temperature change rate at multiple moments in the scheduling period based on the device temperature derivative function;

[0147] A device temperature identification result is determined according to the device temperature and the device temperature change rate.

[0148] For example, the device temperature and the moments in the scheduling cycle are fitted to obtain a device temperature function that describes the change pattern of the device temperature over time in the current detection cycle; the device temperature function is differentiated to determine the device temperature derivative function; multiple moments in the scheduling cycle are substituted into the device temperature derivative function to determine the device temperature change rate at multiple moments in the scheduling cycle; based on the device temperature and the device temperature change rate, the temperature condition of the device is evaluated to determine the device temperature identification result.

[0149] According to one embodiment of the present invention, determining a device temperature identification result according to the device temperature and the device temperature change rate includes:

[0150] Obtain the ambient temperature at multiple times during the scheduling cycle using the temperature sensor installed on the unmanned equipment;

[0151] Determining a preset temperature threshold according to the ambient temperature;

[0152] Determining a first temperature recognition result according to the preset temperature threshold and the device temperature;

[0153] determining a second temperature identification result according to the device temperature change rate and a preset device temperature change rate threshold;

[0154] A device temperature recognition result is determined according to the first temperature recognition result and the second temperature recognition result.

[0155] For example, the temperature sensor of the device in the unmanned device is used to obtain the ambient temperature of the working environment of the unmanned device; according to the ambient temperature, the preset temperature threshold is determined, 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, and 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, it indicates that the temperature of the unmanned device is normal, and the first temperature recognition result is 1. When the temperature change rate threshold is exceeded, it indicates that the unmanned equipment has abnormal temperature rise and there may be a fault. The second temperature recognition result is -1. When the equipment temperature change rate is less than or equal to the preset equipment temperature change rate threshold, it indicates that there is no abnormal temperature rise in the unmanned equipment. The second temperature recognition result is 1. The preset equipment 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 equipment 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 equipment temperature recognition result is 1, indicating that when the temperature change rate is too large or the equipment temperature is too high, the equipment temperature recognition result is -1. When the temperature change rate and the equipment temperature are both within the normal range, the equipment temperature recognition result is 1.

[0156] According to an embodiment of the present invention, in the fault recovery module, it is configured to determine a fault recovery scheduling result according to the equipment health operation coefficient.

[0157] For example, if the equipment health coefficient is less than 3, it means that the unmanned equipment may have a fault, then the fault recovery scheduling result is 1, and fault recovery scheduling is required. If the equipment health coefficient is equal to 3, it means that the unmanned equipment does not have a fault, then the fault recovery scheduling result is 0, indicating that no fault recovery scheduling is required.

[0158] According to an embodiment of the present invention, in the scheduling policy module, a scheduling policy is determined according to the priority scheduling task and the fault recovery scheduling result.

[0159] For example, at each moment in the scheduling cycle, priority scheduling tasks are scheduled, and the unmanned equipment performing the tasks is monitored in real time. When the fault recovery scheduling result of the unmanned equipment is 1, it means that the unmanned equipment has a fault. Among the unscheduled unmanned equipment, an equipment with the same or similar load capacity and power as the unmanned equipment with the fault recovery scheduling result of 1 is selected, and the selected unmanned equipment is sent to the possible unmanned equipment to help continue the task.

[0160] According to an embodiment of the present invention, the drone swarm control system can accurately obtain task information and equipment information of unmanned equipment, and determine the equipment requirement plan for each task based on the equipment information and task information. Furthermore, the resource utilization status of the equipment requirement plan for each task can be analyzed, and the scheduling order of each task can be determined based on the resource utilization status and task information. On the other hand, the healthy working status of the unmanned equipment can be monitored and analyzed in real time during the scheduling process, and the need for recovery scheduling for unmanned equipment failures can be determined based on the healthy working status, thereby improving the utilization rate of unmanned equipment resources and the completion rate of tasks. When determining the task priority scheduling coefficient, the task priority scheduling coefficient can be determined based on the task required load, task required 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 based on three aspects: pre-set priority status, resource usage status, and resource overload status. While meeting the urgent needs of the task, the utilization rate of unmanned equipment resources is improved, and the occurrence of unmanned equipment resource deadlock is reduced, thereby improving the comprehensiveness and accuracy of the task priority scheduling coefficient.

[0161] Figure 2 The following is a flow chart of a method for controlling a drone swarm according to an embodiment of the present invention, wherein the method includes:

[0162] Step S1, obtaining task information of a task at multiple moments in a scheduling cycle, wherein the task information includes: basic task information and task requirement information;

[0163] Step S2: acquiring device information of the unmanned device at multiple times during the scheduling period, wherein the device information includes: device status information and device function information;

[0164] Step S3, determining a task requirement equipment solution based on the task requirement information and the equipment information;

[0165] Step S4, determining the load utilization coefficient and power utilization coefficient of the equipment solution required by the task;

[0166] Step S5, determining a task priority scheduling coefficient based on the task basic information, the load utilization coefficient, and the power utilization coefficient;

[0167] Step S6, determining the priority scheduling task according to the task priority scheduling coefficient;

[0168] Step S7, determining the equipment health coefficient of the scheduled equipment according to the equipment status information;

[0169] Step S8, determining a fault recovery scheduling result based on the equipment health operation coefficient;

[0170] Step S9: determining a scheduling strategy according to the priority scheduling task and the fault recovery scheduling result.

[0171] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0172] Those skilled in the art will appreciate that the embodiments of the present invention described above and shown in the accompanying drawings are intended to be illustrative only and are not intended to limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and illustrated in the embodiments. Any variations or modifications may be made to the embodiments of the present invention without departing from the principles described.

Claims

1. A drone cluster control system, characterized in that: include: The task information module is used to obtain task information of the task at multiple times in the scheduling cycle, wherein the task information includes: basic task information and task requirement information; The device information module is used to obtain device information of unmanned devices at multiple times during the scheduling cycle, wherein the device information includes: device status information and device function information; An equipment solution module, configured to determine a task requirement equipment solution based on the task requirement information and the equipment information; A utilization coefficient module, used to determine the load utilization coefficient and power utilization coefficient of the equipment solution required by the mission; A scheduling coefficient module, configured to determine a task priority scheduling coefficient based on the task basic information, the load utilization coefficient, and the power utilization coefficient; A scheduling sequence module, used for determining priority scheduling tasks according to the task priority scheduling coefficient; An operation coefficient module, configured to determine a device health operation coefficient of a scheduled device based on the device status information; A fault recovery module, configured to determine a fault recovery scheduling result based on the health operation coefficient of the equipment; A scheduling strategy module, configured to determine a scheduling strategy based on the priority scheduling task and the fault recovery scheduling result; Determining a mission requirement equipment solution based on the mission requirement information and the equipment information includes: Determining the mission required load and mission required power according to the mission required information; Determining a device idle state according to the device state information; Determining a schedulable device according to the idle state of the device; Determining, based on the device function information, the dispatchable unmanned device load and the dispatchable unmanned device power of the plurality of dispatchable devices; Determining a mission-required equipment solution based on the mission-required load, the mission-required power, the dispatchable unmanned equipment load, and the dispatchable unmanned equipment power; Determine the load utilization factor and power utilization factor of the equipment solution required by the mission, including: Determine the expected equipment load and expected equipment power of the mission required equipment solution; Determining a load utilization coefficient of a mission-required equipment solution based on the expected equipment load and the mission-required load; Determining a power utilization coefficient of a mission-required equipment solution based on the expected equipment power and the mission-required power; 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 and a total schedulable power according to the schedulable unmanned equipment load and the schedulable unmanned equipment power; Determine the expected task duration based on the basic task information; Determine the task priority based on the basic task information; 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; 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: According to the formula Determine the task priority scheduling coefficient of the kth task at the i-th moment of the scheduling cycle ,in, and is the preset weight, if is the conditional function, or is the logical operator of "or", is the task priority of the kth task, is the load utilization coefficient of the kth task at the i-th moment of the scheduling cycle, is the power utilization coefficient of the kth task at the i-th moment of the scheduling period, is the task load requirement of the kth task, is the task power requirement of the kth task, is the expected task duration of the k-th task, is the total dispatchable load at the i-th moment of the dispatch 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.

2. The UAV cluster control system according to claim 1, characterized in that: Determine the equipment health coefficient of the scheduled equipment based on the equipment status information, including: Determine the device's real-time power level, device temperature, and device signal strength based on the device status information; Determine the device battery life identification result based on the real-time power of the device; Determining a device temperature identification result according to the device temperature; Determining a device signal recognition result according to the device signal strength; The device health coefficient of the scheduled device is determined according to the device life identification result, the device temperature identification result, and the device signal identification result.

3. The UAV swarm control system according to claim 2, characterized in that: Determine the device battery life identification result based on the real-time power level of the device, including: Get the initial power of the unmanned device; Determining the consumed power based on the initial power and the real-time power of the device; Determine the start time of dispatching unmanned equipment; Determining the scheduled time according to the start scheduling time; determining an actual power consumption rate based on the consumed power and the scheduled time; determining an expected power consumption rate based on the expected task duration and the initial power; determining a power consumption identification result according to the actual power consumption rate and the expected power consumption rate; Determine a device power identification result based on the real-time power of the device and a preset device power threshold; Determine a device battery life identification result based on the power consumption identification result and the device power identification result.

4. The UAV cluster control system according to claim 2, characterized in that: Determining a device temperature identification result according to the device temperature includes: Performing fitting based on the device temperature and the time in the scheduling period to obtain a device temperature function of the device temperature in the scheduling period; determining a device temperature derivative function based on the device temperature function; Determining the device temperature change rate at multiple moments in the scheduling period based on the device temperature derivative function; A device temperature identification result is determined according to the device temperature and the device temperature change rate.

5. The UAV cluster control system according to claim 4, characterized in that: Determining a device temperature identification result according to the device temperature and the device temperature change rate includes: Obtain the ambient temperature at multiple times during the scheduling cycle using the temperature sensor installed on the unmanned equipment; Determining a preset temperature threshold according to the ambient temperature; Determining a first temperature recognition result according to the preset temperature threshold and the device temperature; determining a second temperature identification result according to the device temperature change rate and a preset device temperature change rate threshold; A device temperature recognition result is determined according to the first temperature recognition result and the second temperature recognition result.

6. A method for controlling a drone cluster, characterized in that: The method is used in the UAV cluster control system according to any one of claims 1 to 5, comprising: At multiple moments in the scheduling cycle, task information of the task is obtained, wherein the task information includes: basic task information and task requirement information; At multiple moments in the scheduling cycle, device information of the unmanned device is obtained, wherein the device information includes: device status information and device function information; Determining a task requirement equipment solution based on the task requirement information and the equipment information; Determine the load utilization factor and power utilization factor of the equipment solution required by the mission; Determining a task priority scheduling coefficient based on the task basic information, the load utilization coefficient, and the power utilization coefficient; Determining the priority scheduling tasks according to the task priority scheduling coefficient; Determining the equipment health coefficient of the scheduled equipment based on the equipment status information; Determining a fault recovery scheduling result based on the equipment health operation coefficient; A scheduling strategy is determined according to the priority scheduling task and the fault recovery scheduling result.

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