CN116578120A - Unmanned aerial vehicle scheduling method and device, unmanned aerial vehicle system and computer equipment - Google Patents
Unmanned aerial vehicle scheduling method and device, unmanned aerial vehicle system and computer equipment Download PDFInfo
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Abstract
The application relates to an unmanned aerial vehicle scheduling method, an unmanned aerial vehicle scheduling device, an unmanned aerial vehicle system and computer equipment. The method comprises the following steps: determining the flight state of each unmanned aerial vehicle based on flight data of a plurality of unmanned aerial vehicles; determining the environment state of each unmanned aerial vehicle based on the environment data of each unmanned aerial vehicle; based on task requirements, the flight state of each unmanned aerial vehicle and the environmental state, determining that the flight task of each unmanned aerial vehicle is based on the flight task, the flight state of each unmanned aerial vehicle and the environmental state, and generating corresponding control instructions to control each unmanned aerial vehicle to execute the corresponding flight task. By adopting the method, reasonable task allocation and intelligent control can be dynamically carried out, and proper control instructions are generated according to the state and task requirements of the unmanned aerial vehicle, so that a plurality of unmanned aerial vehicles can mutually cooperate and adapt to different environmental changes, and the flight task can be efficiently and stably completed.
Description
Technical Field
The application relates to the field of unmanned aerial vehicle application, in particular to an unmanned aerial vehicle scheduling method, an unmanned aerial vehicle scheduling device, an unmanned aerial vehicle system and computer equipment.
Background
Along with popularization and deployment of fixed hangar mobile hangars and the like, unmanned aerial vehicle application gradually steps into unmanned, and in the existing multi-unmanned aerial vehicle collaborative scheduling method, the unmanned aerial vehicle still stays in a manual stage. In the current one-library multi-unmanned aerial vehicle mode, different unmanned aerial vehicles can be controlled to execute flight tasks by setting different channels. The specific unmanned aerial vehicle executes the flight task and depends on manual arrangement of operators, in the calling process, the specific unmanned model and the scheduling task used belong to a combined task or an independent task, whether the task is intelligently terminated according to local microclimate, whether other unmanned aerial vehicles are needed to assist signal continuation and other specific task arrangement needs the operators to give instructions, and the intelligent degree is low.
Aiming at how to carry out real-time intelligent scheduling on the execution tasks of the unmanned aerial vehicle in the related technology, no effective solution is proposed at present.
Disclosure of Invention
In view of the foregoing, it is desirable to provide an unmanned aerial vehicle scheduling method, apparatus, unmanned aerial vehicle system, and computer device capable of performing real-time intelligent scheduling on an execution task of an unmanned aerial vehicle.
In a first aspect, the application provides a method for scheduling unmanned aerial vehicles. The method comprises the following steps:
Determining the flight state of each unmanned aerial vehicle based on flight data of a plurality of unmanned aerial vehicles; determining the environment state of each unmanned aerial vehicle based on the environment data of each unmanned aerial vehicle;
determining the flight task of each unmanned aerial vehicle based on the task demand, the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle;
based on the flight tasks, the flight states of the unmanned aerial vehicles and the environmental states, corresponding control instructions are generated to control the unmanned aerial vehicles to execute the corresponding flight tasks.
In one embodiment, the determining the flight status of each of the unmanned aerial vehicles based on the flight data of the plurality of unmanned aerial vehicles includes:
acquiring position information, pose information, flight speed information, equipment operation information and equipment configuration information of each unmanned aerial vehicle through a sensor on each unmanned aerial vehicle;
and performing flight state evaluation based on the position information, the pose information, the flight speed information, the equipment operation information and the equipment configuration information, and determining the flight state of each unmanned aerial vehicle.
In one embodiment, the determining, based on the environmental data in which each of the unmanned aerial vehicles is located, an environmental state in which each of the unmanned aerial vehicles is located includes:
And acquiring geographic data and meteorological data of the environments of the unmanned aerial vehicles, and determining the environmental states of the unmanned aerial vehicles based on the geographic data and the meteorological data.
In one embodiment, the determining the flight mission of each unmanned aerial vehicle based on the mission requirement, the flight state of each unmanned aerial vehicle, and the environmental state includes:
determining the priority allocated to the task based on the task demand, the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle;
and determining the flight task of each unmanned aerial vehicle based on the priority.
In one embodiment, the flight mission comprises planning a flight route, and the flight mission is determined by a path planning model using mission requirements, a flight status of each of the unmanned aerial vehicles, and an environmental status.
In one embodiment, in case of a change in the flight status and/or the environmental status of the unmanned aerial vehicle, the method further comprises:
and re-determining the flight mission of the unmanned aerial vehicle based on the mission requirement, the changed flight state of the unmanned aerial vehicle and/or the changed environment state.
In a second aspect, the present application further provides a scheduling device for a unmanned aerial vehicle, where the device includes:
The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for determining the flight state of each unmanned aerial vehicle based on flight data of a plurality of unmanned aerial vehicles; determining the environment state of each unmanned aerial vehicle based on the environment data of each unmanned aerial vehicle;
the distribution module is used for determining the flight task of each unmanned aerial vehicle based on the task requirement, the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle;
and the execution module is used for generating corresponding control instructions based on the flight tasks, the flight states of the unmanned aerial vehicles and the environment states of the unmanned aerial vehicles so as to control the unmanned aerial vehicles to execute the corresponding flight tasks.
In a third aspect, the present application further provides an unmanned aerial vehicle system, including a plurality of unmanned aerial vehicles and a scheduling device, each unmanned aerial vehicle respectively collects flight data and environmental data where the unmanned aerial vehicle is located, and sends the flight data and the environmental data to the scheduling device, and the scheduling device implements the following steps:
determining the flight state of each unmanned aerial vehicle based on flight data of a plurality of unmanned aerial vehicles; determining the environment state of each unmanned aerial vehicle based on the environment data of each unmanned aerial vehicle;
determining the flight task of each unmanned aerial vehicle based on the task demand, the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle;
Based on the flight tasks, the flight states of the unmanned aerial vehicles and the environmental states, corresponding control instructions are generated to control the unmanned aerial vehicles to execute the corresponding flight tasks.
In a fourth aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
determining the flight state of each unmanned aerial vehicle based on flight data of a plurality of unmanned aerial vehicles; determining the environment state of each unmanned aerial vehicle based on the environment data of each unmanned aerial vehicle;
determining the flight task of each unmanned aerial vehicle based on the task demand, the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle;
based on the flight tasks, the flight states of the unmanned aerial vehicles and the environmental states, corresponding control instructions are generated to control the unmanned aerial vehicles to execute the corresponding flight tasks.
In a fifth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
Determining the flight state of each unmanned aerial vehicle based on flight data of a plurality of unmanned aerial vehicles; determining the environment state of each unmanned aerial vehicle based on the environment data of each unmanned aerial vehicle;
determining the flight task of each unmanned aerial vehicle based on the task demand, the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle;
based on the flight tasks, the flight states of the unmanned aerial vehicles and the environmental states, corresponding control instructions are generated to control the unmanned aerial vehicles to execute the corresponding flight tasks.
According to the unmanned aerial vehicle scheduling method, the unmanned aerial vehicle scheduling device, the unmanned aerial vehicle system and the computer equipment, the flight state of each unmanned aerial vehicle is determined based on flight data of a plurality of unmanned aerial vehicles; determining the environment state of each unmanned aerial vehicle based on the environment data of each unmanned aerial vehicle; determining the flight task of each unmanned aerial vehicle based on the task demand, the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle; based on the flight tasks, the flight states of the unmanned aerial vehicles and the environmental states, corresponding control instructions are generated to control the unmanned aerial vehicles to execute the corresponding flight tasks. Reasonable task allocation and intelligent control can be dynamically carried out, and proper control instructions are generated according to the states and task demands of the unmanned aerial vehicle, so that a plurality of unmanned aerial vehicles can mutually cooperate and adapt to different environmental changes, and flight tasks are efficiently and stably completed.
Drawings
Fig. 1 is an application environment diagram of a method for unmanned aerial vehicle scheduling in one embodiment;
fig. 2 is a flow chart of a method for scheduling a drone in one embodiment;
FIG. 3 is a block diagram of a drone scheduling apparatus in one embodiment;
fig. 4 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The unmanned aerial vehicle calling method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. Wherein the drone cluster 104 may communicate with the server 102 over a network. The data storage system may store data that the server 102 needs to process. The data storage system may be integrated on the server 102 or may be located on a cloud or other network server. Specifically, the unmanned aerial vehicle cluster 104 may return flight data to the server 102 in real time, and the server 102 may determine a flight state of each unmanned aerial vehicle based on the flight data of a plurality of unmanned aerial vehicles, and determine an environmental state of each unmanned aerial vehicle based on the environmental data of each unmanned aerial vehicle. And then determining the flight task of each unmanned aerial vehicle based on the task demand, the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle. And generating corresponding control instructions based on the flight tasks, the flight states of the unmanned aerial vehicles and the environmental states, and finally sending the generated control instructions to corresponding unmanned aerial vehicle equipment to control the unmanned aerial vehicles to execute the corresponding flight tasks. The server 104 may be implemented as a stand-alone server or a server cluster including a plurality of servers.
Fig. 2 is a flow chart of a method for calling a unmanned aerial vehicle according to an embodiment, specifically including the following steps:
step 202, determining the flight state of each unmanned aerial vehicle based on flight data of a plurality of unmanned aerial vehicles; and determining the environmental state of each unmanned aerial vehicle based on the environmental data of each unmanned aerial vehicle.
An unmanned aerial vehicle, or unmanned aerial vehicle (Unmanned Aerial Vehicle, abbreviated as UAV), is an unmanned aerial vehicle that is maneuvered using a radio remote control device and a self-contained programmed control device. Common unmanned aerial vehicle includes unmanned fixed wing aircraft, unmanned vertical take-off and landing aircraft, unmanned airship, unmanned helicopter, unmanned many rotor crafts, unmanned umbrella wing aircraft etc. equipment, can be applied to fields such as take photo by plane, agriculture, plant protection, miniature self-timer, express delivery transportation, disaster relief, observe wild animal, monitor infectious disease, survey, news report, electric power inspection, disaster relief, film and television shooting.
The unmanned aerial vehicle comprises a unmanned aerial vehicle, wherein flight data of the unmanned aerial vehicle comprise static flight data and dynamic flight data, the static flight data are data capable of reflecting the performance of the unmanned aerial vehicle, the dynamic flight data are real-time data capable of reflecting the current flight attitude, the flight speed and other information of the unmanned aerial vehicle and historical data acquired by the unmanned aerial vehicle when the historical task is completed, and the environmental data comprise weather and topography data of the environment where the unmanned aerial vehicle is located currently. Specifically, the flight data of the unmanned aerial vehicle can be collected and returned in real time through the sensor arranged in the unmanned aerial vehicle body, the environmental data can be obtained in real time through the sensor arranged in the unmanned aerial vehicle body, and the environmental data can be determined based on real-time update data of relevant meteorological software.
Step 204, determining the flight task of each unmanned aerial vehicle based on the task requirement, the flight state of each unmanned aerial vehicle and the environmental state.
It can be appreciated that when determining the flight mission, the unmanned aerial vehicles with different configurations and different flight states are adapted to different flight missions, and in order to ensure that each flight mission can be efficiently executed, a plurality of unmanned aerial vehicles with higher adaptation degree with each flight mission need to be determined according to the flight state of each unmanned aerial vehicle. For a single unmanned aerial vehicle, the single unmanned aerial vehicle can not only finish one flight task within the same time period, and if the aggregation degree of a plurality of current flight tasks in the geographic position is relatively high, the corresponding unmanned aerial vehicle can execute a plurality of flight tasks within the same time period. In addition, if the unmanned aerial vehicle encounters sudden environmental changes and accidents during the task execution process, the unmanned aerial vehicle can cause the interruption of the flight task, for example, sudden stormy weather, the unmanned aerial vehicle can be influenced by accidents such as collision between the unmanned aerial vehicle and birds, and the like, so that the unmanned aerial vehicle can execute the flight task. Therefore, when determining the flight tasks of each unmanned aerial vehicle, not only task allocation is required according to the task requirements of each task, but also the flight states of each unmanned aerial vehicle and the environmental states of the unmanned aerial vehicles are integrated to determine the flight tasks of each unmanned aerial vehicle
The task requirements can be determined according to task data of each task to be executed, which are received by the ground control center, for example, when the current ground control center receives a wild animal task, a mapping task and an electric power inspection task to wait for executing the task, the flight task to be executed by each unmanned aerial vehicle can be determined according to the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle.
Step 206, generating a corresponding control instruction based on the flight task, the flight state of each unmanned aerial vehicle and the environmental state, so as to control each unmanned aerial vehicle to execute the corresponding flight task.
The control instructions are instructions for controlling the flight states of the unmanned aerial vehicles and at least comprise flight routes of the unmanned aerial vehicles, acceleration and/or deceleration control instructions, flight direction control instructions and task execution instructions. For example, for a power inspection task, the unmanned aerial vehicle needs to perform inspection work when flying according to a specified flight route, and can timely return an inspection result to a ground control center when monitoring a power failure and take a photograph for evidence.
In the unmanned aerial vehicle scheduling method, the flight state of each unmanned aerial vehicle is determined based on flight data of a plurality of unmanned aerial vehicles; determining the environment state of each unmanned aerial vehicle based on the environment data of each unmanned aerial vehicle; determining the flight task of each unmanned aerial vehicle based on the task demand, the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle; based on the flight tasks, the flight states of the unmanned aerial vehicles and the environmental states, corresponding control instructions are generated to control the unmanned aerial vehicles to execute the corresponding flight tasks. The flight tasks of the unmanned aerial vehicles can be dynamically determined according to actual conditions, and the control strategy is adjusted to be suitable for making optimal flight tasks according to unused task scenes and task demands, so that intelligent scheduling of the execution tasks of the unmanned aerial vehicles is realized. The unmanned aerial vehicle can be quickly adjusted according to real-time conditions when the unmanned aerial vehicle executes the flight tasks, and appropriate control instructions are generated, so that the unmanned aerial vehicle can stably complete the tasks in different environments, and the stability and reliability of the unmanned aerial vehicle for executing the tasks are improved.
In one embodiment, the determining the flight status of each of the unmanned aerial vehicles based on the flight data of the plurality of unmanned aerial vehicles includes: acquiring position information, pose information, flight speed information, equipment operation information and equipment configuration information of each unmanned aerial vehicle through a sensor on each unmanned aerial vehicle; and performing flight state evaluation based on the position information, the pose information, the flight speed information, the equipment operation information and the equipment configuration information, and determining the flight state of each unmanned aerial vehicle.
Specifically, in this embodiment, the flight state of the unmanned aerial vehicle may be obtained by using a radar, an image sensor, and a positioning device mounted on the unmanned aerial vehicle in combination with a high-precision map. The flight state of the unmanned aerial vehicle at least comprises position information, pose information, flight speed information, equipment operation information and equipment configuration information of the unmanned aerial vehicle. For example, the position information may include the current flight altitude, longitude and latitude positioning information, and the like of the unmanned aerial vehicle, and the pose information may be the current pitch angle, flight attitude, orientation, and the like of the unmanned aerial vehicle, such as the flight elevation angle of upward flight, the dive angle of downward flight, and the deflection angle during turning, and the like. The flight speed information may include current flight speed, acceleration information, etc. of the unmanned aerial vehicle, the device operation information may include current electric quantity information of the unmanned aerial vehicle, and the device configuration information may include information such as a maximum flight speed supported by the unmanned aerial vehicle, a maximum flight height, etc.
Further, after the flight data of the unmanned aerial vehicle are obtained, intelligent analysis can be performed according to the obtained flight data, and the flight state of each unmanned aerial vehicle can be estimated. Optionally, in another embodiment, the flight state of the corresponding unmanned aerial vehicle at the next moment may be predicted according to the acquired flight data of the unmanned aerial vehicle, so that the corresponding flight task may be adjusted in advance according to the predicted result of the flight state of the unmanned aerial vehicle.
In the embodiment, the flight data of the unmanned aerial vehicle are collected to determine the flight state of the unmanned aerial vehicle, and a data basis is provided for the subsequent establishment of the flight tasks, so that the intelligent scheduling algorithm can establish reasonable flight tasks according to the flight states of different unmanned aerial vehicles when determining the flight tasks, and further a foundation is laid for the unmanned aerial vehicle to efficiently complete the flight tasks.
In one embodiment, the determining, based on the environmental data in which each of the unmanned aerial vehicles is located, an environmental state in which each of the unmanned aerial vehicles is located includes: and acquiring geographic data and meteorological data of the environments of the unmanned aerial vehicles, and determining the environmental states of the unmanned aerial vehicles based on the geographic data and the meteorological data.
The geographical data of the environment where the unmanned plane is located includes, for example, information of the terrain of the flight area, such as the height of a mountain peak existing in the flight area, the land occupation area of the lake and river when the unmanned plane spans the lake and river, and the like, and the boundary information of the flight area can be expressed by longitude and latitude. The meteorological data can comprise information such as weather information, weather state, rainfall information, concentration value of PM2.5, humidity, air pressure and the like of the current environment of the unmanned aerial vehicle.
Preferably, the geographic data can be determined through a real-time image returned by an image sensor installed on the unmanned aerial vehicle, and also can be obtained through inquiring the disclosed regional map information and obtaining the data returned by a high-precision map. Meteorological data can be obtained by inquiring meteorological information disclosed by a local meteorological office or a sensor installed on the unmanned aerial vehicle.
In this embodiment, the corresponding environmental state is determined by collecting the environmental data of the unmanned aerial vehicle, and the real-time environmental change is reflected in time, so that the adverse effect of the sudden environmental change on the flight task is avoided, and the smooth completion of the flight task is prevented.
In one embodiment, the determining the flight mission of each unmanned aerial vehicle based on the mission requirement, the flight state of each unmanned aerial vehicle, and the environmental state includes: determining the priority allocated to the task based on the task demand, the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle; and determining the flight task of each unmanned aerial vehicle based on the priority.
The task demands comprise task contents of flight tasks to be distributed, such as daily equipment inspection, fire detection, mountain area search and rescue, signal connection and the like, and the target and task area are monitored.
Specifically, when a plurality of flight person tasks to be distributed exist, unmanned aerial vehicles required to be called by each flight task to be distributed can be determined according to the requirements of each flight task to be distributed, the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle to be distributed. And then, counting all flight tasks to be executed on each unmanned aerial vehicle for each unmanned aerial vehicle device, determining the priority of each flight task to be executed, and determining the flight task of each unmanned aerial vehicle device according to the corresponding priority.
In an exemplary embodiment, it is assumed that there are a task a, a task b and a task c to be allocated currently, the task a is fault inspection of a distribution line, the task b is fault inspection of a wind generating set, the task c is detection of a potential safety hazard in a natural environment, wherein task a and task b belong to different inspection tasks of the same power generation unit, an overlapping area exists between a task area corresponding to the task c and task areas of the task a and the task b, and when the tasks are allocated, unmanned aerial vehicles called by the task a and the task b are determined to be unmanned aerial vehicles A, B, C according to task content, monitoring targets and task areas of each task, and the unmanned aerial vehicle called by the task c is unmanned aerial vehicle A, D, E. For the fine distribution of each task, after receiving the automatic flight tasks of the unmanned aerial vehicles, the ground control center synthesizes the task demands of each task, the difficulty level of the tasks, the flight state and the environment state of each unmanned aerial vehicle, the charging condition of the unmanned aerial vehicle and the battery endurance condition of the unmanned aerial vehicle, divides each task and executes each flight task in a segmented mode. For example, for the task first, it is specifically that the patrol distribution lines #01- #100 pole, the intelligent scheduling algorithm can estimate and correspondingly call the flight time that unmanned aerial vehicle A, B, C corresponds respectively, segment the task first, adopt #01- #10 distribution lines to refine the patrol and be carried out by unmanned aerial vehicle a, #11- #15 distribution lines's refinement patrol is carried out by unmanned aerial vehicle B, #16- #20 distribution lines's refinement patrol is carried out by unmanned aerial vehicle C, unmanned aerial vehicle a can return the hangar and charge after accomplishing #01- #10 distribution lines's refinement patrol, then according to unmanned aerial vehicle a's charge condition, can carry out #21- #30 distribution lines's refinement patrol after its electric quantity is greater than 90%. Meanwhile, for the task B, specifically, whether the #01- #50 wind generating set is damaged or not is determined by analysis of an intelligent scheduling algorithm, the unmanned aerial vehicle A needs to execute the inspection task of the #01- #05 wind generating set, the unmanned aerial vehicle B needs to execute the inspection task of the #06- #10 wind generating set, and the unmanned aerial vehicle C needs to execute the inspection task of the #11- #15 wind generating set. Further, for the task C, according to analysis of the corresponding task area, it is known that the #01- #10 distribution line and the #01- #5 wind generating set are located in the task area of the task C, so that when the task of the task C is distributed, the task area of the task C can be divided into an area A, the detection task of the area A is distributed into an unmanned aerial vehicle A, the remaining task area is divided into an area B and an area C, the unmanned aerial vehicle D and the unmanned aerial vehicle E are integrated, the unmanned aerial vehicle D is determined to detect the area B, and the unmanned aerial vehicle E is determined to detect the area C.
Furthermore, for the unmanned aerial vehicle a, the corresponding tasks to be executed include a task a, a task b and a task c, and when each flight task to be executed is executed, the corresponding task priority can be determined according to the difficulty level of each flight task to be executed and the distribution of the task areas, and the distribution of the task monitoring objects. For example, the distribution area #01- #10 in the first task covers the distribution area #01- #50 wind generating set in the second task, the area a of the third task is larger than the areas of the first task and the second task, and based on the goal of executing a plurality of flight tasks more quickly in a limited electric quantity, the task priority can be determined by the analysis of the intelligent scheduling algorithm: task B is larger than task A and larger than task C. Preferably, after determining the task priority of each task on the unmanned plane a, the unmanned plane a can determine that the single flight task is to execute the task b first, then execute the task a first, and finally execute the task c. After the unmanned aerial vehicle A executes the single flight task, the unmanned aerial vehicle A can return to the hangar for charging, meanwhile, the corresponding tasks to be executed are analyzed again, the task priority of each task to be executed is determined again, and a new single flight task is determined according to the task priority.
It should be noted that, the unmanned aerial vehicle cluster formed by the unmanned aerial vehicles adopts the rechargeable unmanned aerial vehicle, and the rechargeable unmanned aerial vehicle must be fully charged to perform the next flight task when performing the task, which is limited by the battery charging level, and the charging time generally requires 30 minutes at least, and requires 60 minutes on average, so that the mobility of the unmanned aerial vehicle cluster and the usability under emergency are greatly reduced. However, the electric-exchange unmanned aerial vehicle needs to perform the electric-exchange operation on a specified track or by means of a mechanical arm, so that the space required by the electric-exchange unmanned aerial vehicle is usually large, the cost is high, and the system development is difficult. Therefore, the existing rechargeable unmanned aerial vehicle cannot meet the task requirements of flying at any time and any place, and a scheme which can execute the flying task at any time, is low in space cost and low in price is a problem which needs to be solved in the existing unmanned aerial vehicle intelligent scheduling method.
The embodiment of the application is different from a motor-changing type hangar, can alternately execute flight tasks by utilizing different unmanned aerial vehicles, achieves uninterrupted effect, and even belongs to a rechargeable unmanned aerial vehicle cluster,the flying task can be executed at any time, and the required space cost and the price cost are low.
In this embodiment, the task priority of each task is determined by analyzing the task requirement, the flight state of each unmanned aerial vehicle and the environmental state where each unmanned aerial vehicle is located, and then the flight task of the corresponding unmanned aerial vehicle is determined according to the task priority, which is beneficial to formulating more reasonable flight tasks according to the actual requirement of each task and the state of each unmanned aerial vehicle.
In one embodiment, the flight mission comprises planning a flight route, the flight mission being determined by a path planning model using mission requirements, a flight status of each of the unmanned aerial vehicles, and an environmental status.
The flight mission at least comprises a flight route of the unmanned aerial vehicle. Specifically, when each flight task is determined according to the flight state of each unmanned aerial vehicle and the environmental state where the task is located through a path planning model, a heuristic search algorithm can be adopted, the flight route of each flight task is determined by combining methods such as an A-algorithm and a Dijkstra algorithm, and the optimal path is searched by adopting different heuristic strategies, so that the purpose of rapidly and accurately solving the path planning problem is achieved.
Preferably, when the task allocation and the path planning are determined through an intelligent scheduling algorithm, the problems of the task allocation and the path planning can be converted into optimization problems. By carrying out mathematical modeling on the optimization problem, reasonable optimization objective functions and constraint conditions are formulated, and the optimal solution can be solved by utilizing linear programming, integer programming, nonlinear programming and the like. For example, after determining the task to be executed in each unmanned aerial vehicle, if the electric quantity of the unmanned aerial vehicle is currently used as the first requirement, when determining the flight task, the corresponding optimal objective function and constraint condition can be executed based on the current electric quantity of the unmanned aerial vehicle, and the corresponding optimal flight route can be determined. Furthermore, when determining the corresponding path planning model, the membership data of the unmanned aerial vehicle can be learned and analyzed by combining the existing machine learning algorithm, and the decision tree and the neural network algorithm are combined to determine the path planning model which is more suitable for the current unmanned aerial vehicle. Thereby realizing more intelligent and self-adaptive task allocation and path planning.
In the embodiment, the flight state and the environmental state of each unmanned aerial vehicle required by the task are analyzed by means of the path planning model, the corresponding flight task can be determined more intelligently by executing the corresponding optimization objective function and the constraint condition, and the optimal flight route is planned, so that the flight task of each unmanned aerial vehicle is determined rapidly and efficiently, and the optimal task completion efficiency and resource utilization rate are achieved.
In one embodiment, in case of a change in the flight status and/or the environmental status of the drone, the method further comprises: and re-determining the flight mission of the unmanned aerial vehicle based on the mission requirement, the changed flight state of the unmanned aerial vehicle and/or the changed environment state.
In this embodiment, when the unmanned aerial vehicle is controlled to execute the flight task according to the control instruction, the real-time flight state and the environmental state of the unmanned aerial vehicle need to be monitored, and when the flight state and/or the environmental state of the unmanned aerial vehicle change, operations such as state estimation, task allocation, path planning, generation of the control instruction, and the like are performed again.
In an exemplary embodiment, if the current unmanned aerial vehicle encounters sudden weather conditions during the process of executing the flight task, for example, the ground control center receives sudden changes such as wind speed, humidity, air pressure and the like transmitted by the unmanned aerial vehicle executing the inspection task, after the intelligent scheduling algorithm performs analysis and evaluation according to the real-time flight state and the real-time environment state of the unmanned aerial vehicle, it is determined that the residual electric quantity of the current unmanned aerial vehicle is insufficient to support the current unmanned aerial vehicle to complete the residual flight task, a new flight route and a flight task execution sequence can be formulated again according to the residual electric quantity, the real-time flight state, the real-time environment state and the completion state of the current flight task of the unmanned aerial vehicle, the unmanned aerial vehicle is controlled to return to a hangar in advance, the task quantity is shortened, and crash after the unmanned aerial vehicle is insufficient in duration is avoided. And re-executing new flight tasks on the idle unmanned aerial vehicle in the hangar to adapt to real-time change of the environment to complete the rest flight tasks to be executed. Further, a plurality of unmanned aerial vehicle space drawers are reserved in the unmanned aerial vehicle library, and the addition of a new unmanned aerial vehicle control channel can be supported, so that a new unmanned aerial vehicle device can be added to execute a flight task, and unmanned aerial vehicle expansion is realized.
Preferably, different state estimation and control algorithms can be adopted according to different unmanned aerial vehicle models and control requirements, and the unmanned aerial vehicle flight state is predicted by combining an extended Kalman filtering algorithm, a PID control algorithm and the like.
In another exemplary embodiment, when the ground control center receives a plurality of unmanned aerial vehicle automatic driving tasks, the heights of the first take-off point and the last take-off point reaching the destination can be estimated automatically according to the ground elevation of the task area. In the fixed hangar inspection mode of current unmanned aerial vehicle, can only keep absolute height between above-mentioned two points, because the in-process of flight is not kept away the barrier, unmanned aerial vehicle is in the execution process of flight task, often because the unreasonable of setting up the height, causes unmanned aerial vehicle electricity to waste, perhaps can't pass the task failure that the mountain along way led to. In the embodiment of the application, the intelligent control algorithm can be adopted to automatically determine the flight height of each unmanned aerial vehicle according to the highest altitude of the path required to pass along the way and the straight line formed by connecting the task point and the departure point, and a reasonable flight route is established.
In another exemplary embodiment, when the unmanned aerial vehicle a is executing the electric power inspection task, if the ground control center receives the mapping task newly, the part of the mapping task corresponding to the task area overlaps with the task area of the electric power training task, and at this time, the intelligent scheduling algorithm may synthesize the current flight state, the environmental state, and the task requirements of the mapping task and the electric power inspection task of the unmanned aerial vehicle a to re-determine the flight task of the unmanned aerial vehicle a
In this embodiment, when the flight state of the unmanned aerial vehicle and/or the environmental state where the unmanned aerial vehicle is located change, the existing control resources can be dynamically scheduled through the intelligent scheduling algorithm, so that multiple unmanned aerial vehicles can mutually cooperate to efficiently complete multiple flight tasks, the task completion efficiency is improved, and when the emergency is faced, the task can be timely adjusted, so that the stability and reliability of the unmanned aerial vehicle for completing the task can be improved, and the accuracy of the flight task can be guaranteed.
Optionally, the flight route of the unmanned plane in the application is different from the flight route of the task execution process. In the course of the return voyage, the unmanned plane can rapidly and with low energy consumption is required to be used as the main planning of the corresponding optimal return voyage flight route.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides an unmanned aerial vehicle dispatching device for realizing the unmanned aerial vehicle dispatching method. The implementation scheme of the solution to the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitation in the embodiments of the unmanned aerial vehicle scheduling device or devices provided below may be referred to the limitation of the unmanned aerial vehicle scheduling method hereinabove, and will not be repeated here.
Fig. 3 is a schematic structural diagram of a scheduling device for a unmanned aerial vehicle according to an embodiment, as shown in fig. 3, including: an acquisition module 31, a distribution module 32 and an execution module 33, wherein:
the acquisition module 32 determines a flight state of each of the unmanned aerial vehicles based on flight data of the plurality of unmanned aerial vehicles, and determines an environmental state of each of the unmanned aerial vehicles based on environmental data of each of the unmanned aerial vehicles.
The allocation module 32 determines the flight mission of each unmanned aerial vehicle based on the mission requirement, the flight status of each unmanned aerial vehicle, and the environmental status.
The execution module 33 generates a corresponding control instruction based on the flight task, the flight state of each unmanned aerial vehicle and the environmental state, so as to control each unmanned aerial vehicle to execute the corresponding flight task.
In the unmanned aerial vehicle scheduling device, the flight state of each unmanned aerial vehicle is determined based on flight data of a plurality of unmanned aerial vehicles; determining the environment state of each unmanned aerial vehicle based on the environment data of each unmanned aerial vehicle; determining the flight task of each unmanned aerial vehicle based on the task demand, the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle; based on the flight tasks, the flight states of the unmanned aerial vehicles and the environmental states, corresponding control instructions are generated to control the unmanned aerial vehicles to execute the corresponding flight tasks. The flight tasks of the unmanned aerial vehicles can be dynamically determined according to actual conditions, and the control strategy is adjusted to be suitable for making optimal flight tasks according to unused task scenes and task demands, so that intelligent scheduling of the execution tasks of the unmanned aerial vehicles is realized. The unmanned aerial vehicle can be quickly adjusted according to real-time conditions when the unmanned aerial vehicle executes the flight tasks, and appropriate control instructions are generated, so that the unmanned aerial vehicle can stably complete the tasks in different environments, and the stability and reliability of the unmanned aerial vehicle for executing the tasks are improved.
Further, the acquisition module 31 is further configured to acquire, through sensors on each of the unmanned aerial vehicles, position information, pose information, flight speed information, equipment operation information, and equipment configuration information of each of the unmanned aerial vehicles;
And performing flight state evaluation based on the position information, the pose information, the flight speed information, the equipment operation information and the equipment configuration information, and determining the flight state of each unmanned aerial vehicle.
Further, the collection module 31 is further configured to obtain geographic data and meteorological data of an environment where each unmanned aerial vehicle is located, and determine an environmental state where each unmanned aerial vehicle is located based on the geographic data and the meteorological data.
Further, the allocation module 32 is further configured to determine a priority of task allocation based on a task requirement, a flight status of each unmanned aerial vehicle, and an environmental status of the unmanned aerial vehicle;
and determining the flight task of each unmanned aerial vehicle based on the priority.
Further, the flight mission comprises a planned flight route, and the flight mission is determined by a path planning model by using mission requirements, the flight state of each unmanned aerial vehicle and the environmental state.
Further, the allocation module 32 is further configured to re-determine the flight mission of the unmanned aerial vehicle based on the mission requirement, the changing flight status of the unmanned aerial vehicle, and/or the changing environmental status.
The various modules in the unmanned aerial vehicle scheduling device described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, an unmanned aerial vehicle dispatching system is provided, which comprises a plurality of unmanned aerial vehicles and a dispatching device, wherein each unmanned aerial vehicle respectively collects flight data and environment data where the unmanned aerial vehicle is located and sends the flight data to the dispatching device, and the dispatching device can determine the flight state of each unmanned aerial vehicle based on the flight data of the plurality of unmanned aerial vehicles; determining the environment state of each unmanned aerial vehicle based on the environment data of each unmanned aerial vehicle; determining the flight task of each unmanned aerial vehicle based on the task demand, the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle; based on the flight tasks, the flight states of the unmanned aerial vehicles and the environmental states, corresponding control instructions are generated to control the unmanned aerial vehicles to execute the corresponding flight tasks.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program when executed by a processor implements a drone scheduling method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
determining the flight state of each unmanned aerial vehicle based on flight data of a plurality of unmanned aerial vehicles; determining the environment state of each unmanned aerial vehicle based on the environment data of each unmanned aerial vehicle;
determining the flight task of each unmanned aerial vehicle based on the task demand, the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle;
based on the flight tasks, the flight states of the unmanned aerial vehicles and the environmental states, corresponding control instructions are generated to control the unmanned aerial vehicles to execute the corresponding flight tasks.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
Determining the flight state of each unmanned aerial vehicle based on flight data of a plurality of unmanned aerial vehicles; determining the environment state of each unmanned aerial vehicle based on the environment data of each unmanned aerial vehicle;
determining the flight task of each unmanned aerial vehicle based on the task demand, the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle;
based on the flight tasks, the flight states of the unmanned aerial vehicles and the environmental states, corresponding control instructions are generated to control the unmanned aerial vehicles to execute the corresponding flight tasks.
The user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (10)
1. A method of unmanned aerial vehicle scheduling, the method comprising:
determining the flight state of each unmanned aerial vehicle based on flight data of a plurality of unmanned aerial vehicles; determining the environment state of each unmanned aerial vehicle based on the environment data of each unmanned aerial vehicle;
determining the flight task of each unmanned aerial vehicle based on the task demand, the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle;
Based on the flight tasks, the flight states of the unmanned aerial vehicles and the environmental states, corresponding control instructions are generated to control the unmanned aerial vehicles to execute the corresponding flight tasks.
2. The method of claim 1, wherein the determining a flight status of each of the drones based on flight data of the plurality of drones includes:
acquiring position information, pose information, flight speed information, equipment operation information and equipment configuration information of each unmanned aerial vehicle through a sensor on each unmanned aerial vehicle;
and performing flight state evaluation based on the position information, the pose information, the flight speed information, the equipment operation information and the equipment configuration information, and determining the flight state of each unmanned aerial vehicle.
3. The method of claim 1, wherein determining an environmental state in which each of the drones is located based on environmental data in which each of the drones is located includes:
and acquiring geographic data and meteorological data of the environments of the unmanned aerial vehicles, and determining the environmental states of the unmanned aerial vehicles based on the geographic data and the meteorological data.
4. The method of claim 1, wherein determining the mission of each of the unmanned aerial vehicles based on the mission demand, the flight status of each of the unmanned aerial vehicles, and the environmental status of each of the unmanned aerial vehicles comprises:
Determining the priority allocated to the task based on the task demand, the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle;
and determining the flight task of each unmanned aerial vehicle based on the priority.
5. The method of claim 4, wherein the flight mission comprises planning a flight route, the flight mission being determined by a path planning model using mission requirements, a flight status of each of the unmanned aerial vehicles, and an environmental status.
6. The method according to claim 1, characterized in that in case of a change of the flight status and/or the environmental status of the unmanned aerial vehicle, the method further comprises:
and re-determining the flight mission of the unmanned aerial vehicle based on the mission requirement, the changed flight state of the unmanned aerial vehicle and/or the changed environment state.
7. An unmanned aerial vehicle scheduling device, the device comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for determining the flight state of each unmanned aerial vehicle based on flight data of a plurality of unmanned aerial vehicles; determining the environment state of each unmanned aerial vehicle based on the environment data of each unmanned aerial vehicle;
the distribution module is used for determining the flight task of each unmanned aerial vehicle based on the task requirement, the flight state of each unmanned aerial vehicle and the environmental state of each unmanned aerial vehicle;
And the execution module is used for generating corresponding control instructions based on the flight tasks, the flight states of the unmanned aerial vehicles and the environment states of the unmanned aerial vehicles so as to control the unmanned aerial vehicles to execute the corresponding flight tasks.
8. An unmanned aerial vehicle system comprising a plurality of unmanned aerial vehicles and a scheduling device, each unmanned aerial vehicle collecting respectively flight data and environmental data in which it is located and sending to the scheduling device, the scheduling device implementing the steps of the method of any one of claims 1 to 7.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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| CN117739925B (en) * | 2023-12-19 | 2024-05-24 | 广东省水利水电第三工程局有限公司 | Intelligent image analysis method for unmanned aerial vehicle |
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