Unmanned aerial vehicle cluster marshalling method and system and medium

Abstract

The invention provides an unmanned aerial vehicle cluster marshalling method and system and a medium. The method comprises the steps of obtaining performance requirement data of unmanned aerial vehicle cluster marshalling and processing the performance requirement data to obtain a performance requirement index, obtaining performance attribute data of unmanned aerial vehicles and processing the performance attribute data to obtain a performance evaluation index, and selecting unmanned aerial vehicles to be marshaled after obtaining an effective performance evaluation index through threshold comparison. The unmanned aerial vehicle with the maximum effective performance evaluation index is selected as a leader, and the leader is positioned to the graph center of the unmanned aerial vehicle cluster marshalling; through calculation of the performance requirement index and the performance evaluation index, after threshold comparison is carried out, the unmanned aerial vehicle to be grouped and the leader are selected, and then the unmanned aerial vehicle cluster grouping technology is realized.

Description

Technical Field

[0001] The present application relates to the field of drones, and more specifically, to a method, system and medium for grouping drone clusters. Background Art

[0002] With the continuous development of science and technology, drones are increasingly used in various fields. In some complex mission scenarios, a single drone is often unable to meet the needs. Drone swarming can improve the efficiency of mission execution and enhance the reliability and adaptability of the system. However, the existing drone swarming method has some problems, such as low grouping efficiency, poor flexibility, and insufficient coordination. Therefore, a new drone swarming method is needed to solve these problems.

[0003] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0004] The purpose of this application is to provide a method, system and medium for drone cluster formation, which can select drones to be grouped and leaders after threshold comparison through calculation of performance requirement index and performance evaluation index, thereby realizing the technology of drone cluster formation.

[0005] The present application also provides a method for forming a drone cluster, comprising the following steps: Obtain the performance requirement information of the UAV cluster formation, extract the performance requirement data according to the performance requirement information, and process it to obtain the performance requirement index; Acquiring performance attribute data of the drone, processing the performance attribute data to obtain a performance evaluation index, and comparing the performance evaluation index with the performance requirement index to obtain an effective performance evaluation index; Obtain the number of drones corresponding to the effective performance evaluation index, compare it with the number of drones required for the preset mission, and select the drones to be assembled; The drones to be grouped are sorted according to the effective performance evaluation index to obtain the optimal effective performance evaluation index, and the corresponding drone is used as the leader of the drone cluster grouping, and the leader is positioned at the center of the drone cluster grouping graphic; The drones to be grouped complete the drone cluster formation according to the instructions of the leader.

[0006] Optionally, in the drone swarm grouping method described in the present application, the obtaining of performance requirement information of the drone swarm grouping, extracting performance requirement data according to the performance requirement information and processing to obtain a performance requirement index specifically includes: Obtain the performance requirement information of the drone cluster formation and extract the performance requirement data, including the total operation time requirement data, the positioning accuracy requirement data, the communication stability requirement data and the processor performance requirement data; The total operation time requirement data, positioning accuracy requirement data, communication stability requirement data and processor performance requirement data are input into a preset performance requirement index evaluation model for processing to obtain a performance requirement index.

[0007] Optionally, in the drone swarm grouping method described in the present application, the acquiring of the performance attribute data of the drones, processing the performance attribute data to obtain a performance evaluation index, and comparing the performance evaluation index with the performance requirement index to obtain an effective performance evaluation index specifically includes: Obtain the performance attribute data of the drone, including total running time data, historical positioning accuracy data, historical communication stability data, and configured processor performance data; Inputting the total running time data, historical positioning accuracy data, historical communication stability data, and configuration processor performance data into a preset performance index evaluation model for processing to obtain a performance evaluation index; Comparing the performance evaluation index with the performance requirement index to obtain the performance matching status of the UAV, including a valid performance evaluation index or an invalid performance evaluation index; If the performance evaluation index is greater than the performance requirement index, the performance matching state of the UAV is a valid performance evaluation index.

[0008] Optionally, in the drone cluster grouping method described in the present application, obtaining the number of drones corresponding to the effective performance evaluation index and comparing it with the number of drones required by a preset task to select the drones to be grouped specifically includes: The effective performance evaluation index is counted to obtain the corresponding number of available drones; Compare the number of available drones with the number of drones required for a preset mission by a threshold value to obtain a flight readiness state; If the number of available drones is greater than or equal to the number of drones required for the mission, the flight preparation state is a state that meets the flight conditions; If the number of available drones is less than the number of drones required for the mission, the flight preparation state is a state where the flight conditions are not met; If the flight preparation state satisfies the flight conditions, a drone to be assembled is selected from the available drones.

[0009] Optionally, in the drone swarm grouping method described in the present application, the drones to be grouped are sorted according to the effective performance evaluation index to obtain the optimal effective performance evaluation index, and the corresponding drone is used as the leader of the drone swarm grouping, and the leader is positioned at the graphic center of the drone swarm grouping, specifically including: Sorting the drones to be organized in descending order according to the effective performance evaluation index to obtain a sequential effective performance evaluation index; Selecting the UAV corresponding to the maximum effective performance evaluation index in the sequential effective performance evaluation index as the leader of the UAV cluster formation; Obtain the preset grouping information of the drone cluster grouping and extract the graphic center of the grouping layout; Position the leader in the center of the graph.

[0010] Optionally, the drone cluster formation method described in the present application further includes: Obtain real-time operation data of each drone in the drone cluster, including real-time range data, real-time communication stability data, and real-time positioning accuracy data; Inputting the real-time cruising range data, the real-time communication stability data, and the real-time positioning accuracy data into a preset real-time operation performance evaluation model for processing to obtain a real-time operation performance index; Sorting the real-time operation performance indexes in descending order to obtain the maximum real-time operation performance index; The unattended node corresponding to the maximum real-time operation performance index is used as the real-time leader, and the real-time leader is swapped with the leader.

[0011] In a second aspect, the present application provides a drone cluster grouping system, the system comprising: a memory and a processor, wherein the memory stores a program of a drone cluster grouping method, and when the program of the drone cluster grouping method is executed by the processor, the following steps are implemented: Obtain the performance requirement information of the UAV cluster formation, extract the performance requirement data according to the performance requirement information, and process it to obtain the performance requirement index; Acquiring performance attribute data of the drone, processing the performance attribute data to obtain a performance evaluation index, and comparing the performance evaluation index with the performance requirement index to obtain an effective performance evaluation index; Obtain the number of drones corresponding to the effective performance evaluation index, compare it with the number of drones required for the preset mission, and select the drones to be assembled; The drones to be grouped are sorted according to the effective performance evaluation index to obtain the optimal effective performance evaluation index, and the corresponding drone is used as the leader of the drone cluster grouping, and the leader is positioned at the center of the drone cluster grouping graphic; The drones to be grouped complete the drone cluster formation according to the instructions of the leader.

[0012] Optionally, in the drone swarm grouping system described in the present application, the obtaining of performance requirement information of the drone swarm grouping, extracting performance requirement data according to the performance requirement information and processing to obtain a performance requirement index specifically includes: Obtain the performance requirement information of the drone cluster formation and extract the performance requirement data, including the total operation time requirement data, the positioning accuracy requirement data, the communication stability requirement data and the processor performance requirement data; The total operation time requirement data, positioning accuracy requirement data, communication stability requirement data and processor performance requirement data are input into a preset performance requirement index evaluation model for processing to obtain a performance requirement index.

[0013] Optionally, in the drone swarm grouping system described in the present application, the acquiring of the performance attribute data of the drone, processing the performance attribute data to obtain a performance evaluation index, and comparing the performance evaluation index with the performance requirement index to obtain an effective performance evaluation index specifically includes: Obtain the performance attribute data of the drone, including total running time data, historical positioning accuracy data, historical communication stability data, and configured processor performance data; Inputting the total running time data, historical positioning accuracy data, historical communication stability data, and configuration processor performance data into a preset performance index evaluation model for processing to obtain a performance evaluation index; Comparing the performance evaluation index with the performance requirement index to obtain the performance matching status of the UAV, including a valid performance evaluation index or an invalid performance evaluation index; If the performance evaluation index is greater than the performance requirement index, the performance matching state of the UAV is a valid performance evaluation index.

[0014] In a third aspect, the present application also provides a readable storage medium, which stores a drone cluster grouping method program. When the drone cluster grouping method program is executed by a processor, the steps of a drone cluster grouping method as described in any one of the above items are implemented.

[0015] As can be seen from the above, the application provides a method, system and medium for drone cluster formation. The method obtains the performance requirement data of drone cluster formation and processes it to obtain the performance requirement index, obtains the performance attribute data of the drone and processes it to obtain the performance evaluation index, selects the drone to be grouped after obtaining the effective performance evaluation index through threshold comparison, selects the drone with the maximum effective performance evaluation index as the leader, and locates the leader to the center of the drone cluster formation graphic; thereby, the drone to be grouped and the leader are selected after the threshold comparison is performed through the calculation of the performance requirement index and the performance evaluation index, and the drone to be grouped and the leader are selected, thereby realizing the technology of drone cluster formation.

[0016] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures particularly pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flowchart of a method for grouping drone clusters provided in an embodiment of the present application; Figure 2 A flowchart of obtaining a performance requirement index for a method for grouping drone clusters provided in an embodiment of the present application; Figure 3 A flowchart of obtaining an effective performance evaluation index for a method for grouping drone clusters provided in an embodiment of the present application; Figure 4 A flowchart of selecting drones to be grouped in a method for grouping drones provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0020] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1: is a flow chart of a method for grouping drone swarms in some embodiments of the present application. The method for grouping drone swarms is used in a terminal device, such as a computer, a mobile phone, etc. The method for grouping drone swarms includes the following steps: S11. Acquire performance requirement information of the UAV cluster formation, extract performance requirement data according to the performance requirement information, and process and obtain a performance requirement index; S12, obtaining performance attribute data of the drone, processing the performance attribute data to obtain a performance evaluation index, and comparing the performance evaluation index with the performance requirement index to obtain an effective performance evaluation index; S13, obtaining the number of drones corresponding to the effective performance evaluation index, and comparing it with the number of drones required by the preset task, and selecting the drones to be grouped; S14, sorting the drones to be grouped according to the effective performance evaluation index to obtain the optimal effective performance evaluation index, and using the corresponding drone as the leader of the drone cluster grouping, and positioning the leader at the center of the drone cluster grouping graphic; S15. The drones to be grouped complete the drone cluster grouping according to the leader's instructions.

[0022] It should be noted that when the drone cluster is formed, there are different formation modes and tasks. Different formation modes and tasks will have different requirements for drones. The performance requirement data of the drone cluster formation is obtained and then processed to obtain the performance requirement index of the drone; the performance attribute data of the drone is obtained and processed to obtain the performance evaluation index, and the performance evaluation data is compared with the performance requirement index to obtain the effective performance evaluation index; according to the effective performance evaluation index, the drones that meet the requirements and can perform the flight formation task are obtained, and then the number of drones that can perform the flight formation task is obtained by statistics, and the drones to be formed are selected by comparing with the preset number of drones required for the task; in order to better perform the formation task, the drone corresponding to the optimal effective performance evaluation index is used as the leader of the drone cluster formation. The leader can monitor the formation and task execution status of the drones in the formation, and can also issue task adjustment instructions in time. In order to ensure the transmission of communication and instructions, the leader is positioned at the center of the graphics of the drone several circles. The center of the graphics can be the center of the plane or the center of the three-dimensional graphic. Finally, the drone to be formed completes the drone cluster formation according to the leader's instructions.

[0023] Please refer to Figure 2 , Figure 2 The flowchart of obtaining a performance requirement index of a method for obtaining a grouping of drone swarms provided in an embodiment of the present application is as follows. According to an embodiment of the present invention, obtaining performance requirement information of a grouping of drone swarms, extracting performance requirement data according to the performance requirement information and processing to obtain a performance requirement index specifically includes: S21. Obtain performance requirement information of the drone cluster formation and extract performance requirement data, including total operation time requirement data, positioning accuracy requirement data, communication stability requirement data, and processor performance requirement data; S22, inputting the total operation time requirement data, positioning accuracy requirement data, communication stability requirement data and processor performance requirement data into a preset performance requirement index evaluation model for processing to obtain a performance requirement index.

[0024] It should be noted that the performance requirement data is extracted through the performance requirement information of the UAV cluster formation. The total operation time requirement data refers to the requirements of the UAV cluster formation on the use time of the UAV. When the total operation time of the UAV is too long, the performance or safety of the UAV cannot meet the formation requirements; the positioning accuracy requirement data refers to the requirements for the positioning accuracy of the UAV. According to the formation requirements, it must be higher than the preset value to ensure the normal execution of the task after formation; the communication stability requirement data refers to the requirements for the communication stability of the UAV. Only stable communication can ensure the execution of the UAV cluster formation and subsequent flight tasks; the processor performance requirement data refers to the requirements for the performance of the processor configured for the UAV. In order to ensure the rapid and timely processing of UAV commands, the processor needs to have a higher configuration, which is set autonomously according to the formation requirements; the performance requirement index refers to the data on the comprehensive performance requirements of the UAV when the UAV cluster is formed. The calculation formula of the performance requirement index in the performance requirement index evaluation model is: ; in, is the performance requirement index, , , , They are the total running time requirement data, positioning accuracy requirement data, communication stability requirement data, and processor performance requirement data. , , , It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset UAV cluster grouping platform); the performance requirement index evaluation model is obtained by training a large amount of historical data, and the performance requirement index can be calculated through this model.

[0025] Please refer to Figure 3 , Figure 3 The flowchart of obtaining an effective performance evaluation index of a method for obtaining a grouping of drone swarms provided in an embodiment of the present application. According to an embodiment of the present invention, the acquisition of performance attribute data of drones, processing the performance attribute data to obtain a performance evaluation index, and comparing the performance evaluation index with the performance requirement index to obtain an effective performance evaluation index specifically includes: S31. Obtaining performance attribute data of the drone, including total running time data, historical positioning accuracy data, historical communication stability data, and configured processor performance data; S32, inputting the total running time data, historical positioning accuracy data, historical communication stability data, and configuration processor performance data into a preset performance index evaluation model for processing to obtain a performance evaluation index; S33, comparing the performance evaluation index with the performance requirement index to obtain the performance matching status of the UAV, including a valid performance evaluation index or an invalid performance evaluation index; S34. If the performance evaluation index is greater than the performance requirement index, the performance matching state of the UAV is a valid performance evaluation index.

[0026] It should be noted that in order to evaluate whether the drone meets the requirements of drone cluster formation, it is necessary to obtain the performance attribute data of the drone, including the total running time data, historical positioning accuracy data, historical communication stability data and configuration processor performance data. The performance evaluation index is obtained according to the performance attribute data processing. The calculation formula of the performance evaluation index in the performance index evaluation model is: ; in, is the performance evaluation index, , , , They are total running time data, historical positioning accuracy data, historical communication stability data, and configured processor performance data; After obtaining the performance evaluation index, compare it with the performance requirement index. Only UAVs with a performance evaluation index greater than the performance requirement index can meet the UAV formation requirements. In this case, the UAV performance matches the formation requirements, and the corresponding performance evaluation index is a valid evaluation index, otherwise it is an invalid performance evaluation index.

[0027] Please refer to Figure 4 , Figure 4 The flowchart of selecting drones to be grouped in a method for grouping drones provided in an embodiment of the present application is as follows. According to an embodiment of the present invention, the number of drones corresponding to the effective performance evaluation index is obtained, and compared with the number of drones required by the preset task, and the drones to be grouped are selected, specifically including: S41, collecting statistics on the effective performance evaluation index to obtain the corresponding number of available drones; S42, comparing the number of available drones with the number of drones required by a preset mission by a threshold value to obtain a flight preparation state; S43: If the number of available drones is greater than or equal to the number of drones required for the mission, the flight preparation state is a state that satisfies the flight conditions; S44: If the number of available drones is less than the number of drones required for the mission, the flight preparation state is a state of not meeting the flight conditions; S45. If the flight preparation state satisfies the flight conditions, a drone to be assembled is selected from the available drones.

[0028] It should be noted that after obtaining the drone performance evaluation status, a quantitative count is performed to obtain the corresponding number of drones that can be used for grouping. If the number of available drones is greater than the number of drones required for the mission, it means that both the performance and the number can meet the drone cluster grouping. The flight preparation state is the drone cluster preparation state for executing the drone cluster grouping flight mission, including the state of meeting the flight conditions or the state of not meeting the flight conditions. If the number of available drones is greater than or equal to the number of drones required for the mission, the flight preparation state is the state of meeting the flight conditions, and the drones that perform the grouping are selected from the available drones as the drones to be grouped.

[0029] According to an embodiment of the present invention, the method of sorting the drones to be grouped according to the effective performance evaluation index to obtain the optimal effective performance evaluation index, and using the corresponding drone as the leader of the drone cluster grouping, and positioning the leader to the graphic center of the drone cluster grouping specifically includes: Sorting the drones to be organized in descending order according to the effective performance evaluation index to obtain a sequential effective performance evaluation index; Selecting the UAV corresponding to the maximum effective performance evaluation index in the sequential effective performance evaluation index as the leader of the UAV cluster formation; Obtain the preset grouping information of the drone cluster grouping and extract the graphic center of the grouping layout; Position the leader in the center of the graph.

[0030] It should be noted that when executing drone cluster formation, in order to communicate more efficiently and execute tasks more accurately, a drone with the best performance should be selected as the leader of the formation. The leader can detect the dynamics of each drone in the formation in time, and can also process and issue instructions for adjustment after an emergency occurs. The drones to be grouped are sorted in descending order according to the effective performance evaluation index to obtain the sequential effective performance evaluation index, and the drone corresponding to the maximum effective performance evaluation index is selected as the leader of the drone cluster formation. The leader is positioned at the center of the graphic of the formation layout, which can be the center of a plane geometric figure or the center of a three-dimensional figure, and is set specifically according to the situation of the formation or task execution.

[0031] According to an embodiment of the present invention, it also includes: Obtain real-time operation data of each drone in the drone cluster, including real-time range data, real-time communication stability data, and real-time positioning accuracy data; Inputting the real-time cruising range data, the real-time communication stability data, and the real-time positioning accuracy data into a preset real-time operation performance evaluation model for processing to obtain a real-time operation performance index; Sorting the real-time operation performance indexes in descending order to obtain the maximum real-time operation performance index; The unattended node corresponding to the maximum real-time operation performance index is used as the real-time leader, and the real-time leader is swapped with the leader.

[0032] It should be noted that after the drone is formed or in the process of performing a task, there may be a certain probability of change. This change will cause the drone as the leader to no longer be suitable to continue to serve as the leader. In order to ensure the stability of the formation or the smooth execution of the task, the leader needs to be replaced, so the real-time operation data of the drone must be obtained; in this embodiment, for battery-powered drones, the real-time cruising range data refers to the real-time remaining cruising range of the drone. Since the leader needs to process data in real time, the processor workload is large, which will accelerate power consumption. Therefore, the cruising range during flight is one of the factors considered by the leader; the real-time operation performance evaluation model is obtained by training a large amount of historical data, and the real-time operation performance index can be calculated through the model; the calculation formula of the real-time operation performance index in the real-time operation performance evaluation model is: ; in, To run the performance index in real time, , , They are real-time mileage data, real-time communication stability data, and real-time positioning accuracy data. , , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset UAV cluster grouping platform).

[0033] It is worth mentioning that according to an embodiment of the present invention, it also includes: The real-time leader establishes a communication connection with the drones in the drone cluster formation and activates a preset task management program; During the communication process, obtain the real-time communication effect data of each connected drone, including real-time radio communication effect data, real-time satellite communication effect data, and real-time Wi-Fi communication effect data; sorting the real-time radio communication effect data, the real-time satellite communication effect data, and the real-time Wi-Fi communication effect data in descending order to obtain optimal real-time communication effect data; The real-time leader communicates with each corresponding drone in accordance with the communication method corresponding to the optimal real-time communication effect data.

[0034] It should be noted that there are many ways for drones to communicate, and the effects of various methods will vary depending on the actual situation. Therefore, choosing the best communication method will improve communication efficiency and effect. The communication effect data reflects the comprehensive effect data of communication, taking into account the transmission rate, stability and communication delay data. By sorting the real-time radio communication effect data, real-time satellite communication effect data and real-time Wi-Fi communication effect data, and then selecting the maximum value, the maximum value indicates the best communication effect.

[0035] The present invention also discloses a drone cluster grouping system, comprising a memory and a processor, wherein the memory stores a drone cluster grouping method program, and when the drone cluster grouping method program is executed by the processor, the following steps are implemented: Obtain the performance requirement information of the UAV cluster formation, extract the performance requirement data according to the performance requirement information, and process it to obtain the performance requirement index; Acquiring performance attribute data of the drone, processing the performance attribute data to obtain a performance evaluation index, and comparing the performance evaluation index with the performance requirement index to obtain an effective performance evaluation index; Obtain the number of drones corresponding to the effective performance evaluation index, compare it with the number of drones required for the preset mission, and select the drones to be assembled; The drones to be grouped are sorted according to the effective performance evaluation index to obtain the optimal effective performance evaluation index, and the corresponding drone is used as the leader of the drone cluster grouping, and the leader is positioned at the center of the drone cluster grouping graphic; The drones to be grouped complete the drone cluster formation according to the instructions of the leader.

[0036] It should be noted that when the drone cluster is formed, there are different formation modes and tasks. Different formation modes and tasks will have different requirements for drones. The performance requirement data of the drone cluster formation is obtained and then processed to obtain the performance requirement index of the drone; the performance attribute data of the drone is obtained and processed to obtain the performance evaluation index, and the performance evaluation data is compared with the performance requirement index to obtain the effective performance evaluation index; according to the effective performance evaluation index, the drones that meet the requirements and can perform the flight formation task are obtained, and then the number of drones that can perform the flight formation task is obtained by statistics, and the drones to be formed are selected by comparing with the preset number of drones required for the task; in order to better perform the formation task, the drone corresponding to the optimal effective performance evaluation index is used as the leader of the drone cluster formation. The leader can monitor the formation and task execution status of the drones in the formation, and can also issue task adjustment instructions in time. In order to ensure the transmission of communication and instructions, the leader is positioned at the center of the graphics of the drone several circles. The center of the graphics can be the center of the plane or the center of the three-dimensional graphic. Finally, the drone to be formed completes the drone cluster formation according to the leader's instructions.

[0037] According to an embodiment of the present invention, the obtaining of performance requirement information of the drone cluster formation, extracting performance requirement data according to the performance requirement information and processing to obtain a performance requirement index specifically includes: Obtain the performance requirement information of the drone cluster formation and extract the performance requirement data, including the total operation time requirement data, the positioning accuracy requirement data, the communication stability requirement data and the processor performance requirement data; The total operation time requirement data, positioning accuracy requirement data, communication stability requirement data and processor performance requirement data are input into a preset performance requirement index evaluation model for processing to obtain a performance requirement index.

[0038] It should be noted that the performance requirement data is extracted through the performance requirement information of the UAV cluster formation. The total operation time requirement data refers to the requirements of the UAV cluster formation on the use time of the UAV. When the total operation time of the UAV is too long, the performance or safety of the UAV cannot meet the formation requirements; the positioning accuracy requirement data refers to the requirements for the positioning accuracy of the UAV. According to the formation requirements, it must be higher than the preset value to ensure the normal execution of the task after formation; the communication stability requirement data refers to the requirements for the communication stability of the UAV. Only stable communication can ensure the execution of the UAV cluster formation and subsequent flight tasks; the processor performance requirement data refers to the requirements for the performance of the processor configured for the UAV. In order to ensure the rapid and timely processing of UAV commands, the processor needs to have a higher configuration, which is set autonomously according to the formation requirements; the performance requirement index refers to the data on the comprehensive performance requirements of the UAV when the UAV cluster is formed. The calculation formula of the performance requirement index in the performance requirement index evaluation model is: ; in, is the performance requirement index, , , , They are the total running time requirement data, positioning accuracy requirement data, communication stability requirement data, and processor performance requirement data. , , , It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset UAV cluster grouping platform); the performance requirement index evaluation model is obtained by training a large amount of historical data, and the performance requirement index can be calculated through this model.

[0039] According to an embodiment of the present invention, the acquiring of the performance attribute data of the drone, processing the performance attribute data to obtain a performance evaluation index, and comparing the performance evaluation index with the performance requirement index to obtain an effective performance evaluation index specifically includes: Obtain the performance attribute data of the drone, including total running time data, historical positioning accuracy data, historical communication stability data, and configured processor performance data; Inputting the total running time data, historical positioning accuracy data, historical communication stability data, and configuration processor performance data into a preset performance index evaluation model for processing to obtain a performance evaluation index; Comparing the performance evaluation index with the performance requirement index to obtain the performance matching status of the UAV, including a valid performance evaluation index or an invalid performance evaluation index; If the performance evaluation index is greater than the performance requirement index, the performance matching state of the UAV is a valid performance evaluation index.

[0040] It should be noted that in order to evaluate whether the drone meets the requirements of drone cluster formation, it is necessary to obtain the performance attribute data of the drone, including the total running time data, historical positioning accuracy data, historical communication stability data and configuration processor performance data. The performance evaluation index is obtained according to the performance attribute data processing. The calculation formula of the performance evaluation index in the performance index evaluation model is: ; in, is the performance evaluation index, , , , They are total running time data, historical positioning accuracy data, historical communication stability data, and configured processor performance data; After obtaining the performance evaluation index, compare it with the performance requirement index. Only UAVs with a performance evaluation index greater than the performance requirement index can meet the UAV formation requirements. In this case, the UAV performance matches the formation requirements, and the corresponding performance evaluation index is a valid evaluation index, otherwise it is an invalid performance evaluation index.

[0041] According to an embodiment of the present invention, obtaining the number of drones corresponding to the effective performance evaluation index and comparing it with the number of drones required by a preset task to select the drones to be grouped specifically includes: The effective performance evaluation index is counted to obtain the corresponding number of available drones; Compare the number of available drones with the number of drones required for a preset mission by a threshold value to obtain a flight readiness state; If the number of available drones is greater than or equal to the number of drones required for the mission, the flight preparation state is a state that meets the flight conditions; If the number of available drones is less than the number of drones required for the mission, the flight preparation state is a state where the flight conditions are not met; If the flight preparation state satisfies the flight conditions, a drone to be assembled is selected from the available drones.

[0042] It should be noted that after obtaining the drone performance evaluation status, a quantitative count is performed to obtain the corresponding number of drones that can be used for grouping. If the number of available drones is greater than the number of drones required for the mission, it means that both the performance and the number can meet the drone cluster grouping. The flight preparation state is the drone cluster preparation state for executing the drone cluster grouping flight mission, including the state of meeting the flight conditions or the state of not meeting the flight conditions. If the number of available drones is greater than or equal to the number of drones required for the mission, the flight preparation state is the state of meeting the flight conditions, and the drones that perform the grouping are selected from the available drones as the drones to be grouped.

[0043] According to an embodiment of the present invention, the method of sorting the drones to be grouped according to the effective performance evaluation index to obtain the optimal effective performance evaluation index, and using the corresponding drone as the leader of the drone cluster grouping, and positioning the leader to the graphic center of the drone cluster grouping specifically includes: Sorting the drones to be organized in descending order according to the effective performance evaluation index to obtain a sequential effective performance evaluation index; Selecting the UAV corresponding to the maximum effective performance evaluation index in the sequential effective performance evaluation index as the leader of the UAV cluster formation; Obtain the preset grouping information of the drone cluster grouping and extract the graphic center of the grouping layout; Position the leader in the center of the graph.

[0044] It should be noted that when executing drone cluster formation, in order to communicate more efficiently and execute tasks more accurately, a drone with the best performance should be selected as the leader of the formation. The leader can detect the dynamics of each drone in the formation in time, and can also process and issue instructions for adjustment after an emergency occurs. The drones to be grouped are sorted in descending order according to the effective performance evaluation index to obtain the sequential effective performance evaluation index, and the drone corresponding to the maximum effective performance evaluation index is selected as the leader of the drone cluster formation. The leader is positioned at the center of the graphic of the formation layout, which can be the center of a plane geometric figure or the center of a three-dimensional figure, and is set specifically according to the situation of the formation or task execution.

[0045] According to an embodiment of the present invention, it also includes: Obtain real-time operation data of each drone in the drone cluster, including real-time range data, real-time communication stability data, and real-time positioning accuracy data; Inputting the real-time cruising range data, the real-time communication stability data, and the real-time positioning accuracy data into a preset real-time operation performance evaluation model for processing to obtain a real-time operation performance index; Sorting the real-time operation performance indexes in descending order to obtain the maximum real-time operation performance index; The unattended node corresponding to the maximum real-time operation performance index is used as the real-time leader, and the real-time leader is swapped with the leader.

[0046] It should be noted that after the drone is formed or in the process of performing a task, there may be a certain probability of change. This change will cause the drone as the leader to no longer be suitable to continue to serve as the leader. In order to ensure the stability of the formation or the smooth execution of the task, the leader needs to be replaced, so the real-time operation data of the drone must be obtained; in this embodiment, for battery-powered drones, the real-time cruising range data refers to the real-time remaining cruising range of the drone. Since the leader needs to process data in real time, the processor workload is large, which will accelerate power consumption. Therefore, the cruising range during flight is one of the factors considered by the leader; the real-time operation performance evaluation model is obtained by training a large amount of historical data, and the real-time operation performance index can be calculated through the model; the calculation formula of the real-time operation performance index in the real-time operation performance evaluation model is: ; in, To run the performance index in real time, , , They are real-time mileage data, real-time communication stability data, and real-time positioning accuracy data. , , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset UAV cluster grouping platform).

[0047] It is worth mentioning that according to an embodiment of the present invention, it also includes: The real-time leader establishes a communication connection with the drones in the drone cluster formation and activates a preset task management program; During the communication process, obtain the real-time communication effect data of each connected drone, including real-time radio communication effect data, real-time satellite communication effect data, and real-time Wi-Fi communication effect data; sorting the real-time radio communication effect data, the real-time satellite communication effect data, and the real-time Wi-Fi communication effect data in descending order to obtain optimal real-time communication effect data; The real-time leader communicates with each corresponding drone in accordance with the communication method corresponding to the optimal real-time communication effect data.

[0048] It should be noted that there are many ways for drones to communicate, and the effects of various methods will vary depending on the actual situation. Therefore, choosing the best communication method will improve communication efficiency and effect. The communication effect data reflects the comprehensive effect data of communication, taking into account the transmission rate, stability and communication delay data. By sorting the real-time radio communication effect data, real-time satellite communication effect data and real-time Wi-Fi communication effect data, and then selecting the maximum value, the maximum value indicates the best communication effect.

[0049] A third aspect of the present invention provides a readable storage medium, which includes a drone cluster grouping method program. When the drone cluster grouping method program is executed by a processor, it implements the steps of a drone cluster grouping method as described in any one of the above items.

[0050] The present invention discloses a method, system and medium for grouping unmanned aerial vehicle clusters, which obtains performance requirement data of unmanned aerial vehicle cluster grouping and processes it to obtain a performance requirement index, obtains performance attribute data of unmanned aerial vehicles and processes it to obtain a performance evaluation index, obtains an effective performance evaluation index through threshold comparison, selects unmanned aerial vehicles to be grouped, selects the unmanned aerial vehicle with the largest effective performance evaluation index as the leader, and locates the leader to the graphic center of the unmanned aerial vehicle cluster grouping; thereby, the unmanned aerial vehicle to be grouped and the leader are selected after threshold comparison through calculation of the performance requirement index and the performance evaluation index, thereby realizing the technology of unmanned aerial vehicle cluster grouping.

[0051] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0052] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0053] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0054] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0055] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A method for forming a drone cluster, characterized in that: include: Obtain the performance requirement information of the UAV cluster formation, extract the performance requirement data according to the performance requirement information, and process it to obtain the performance requirement index; Acquiring performance attribute data of the drone, processing the performance attribute data to obtain a performance evaluation index, and comparing the performance evaluation index with the performance requirement index to obtain an effective performance evaluation index; Obtain the number of drones corresponding to the effective performance evaluation index, compare it with the number of drones required for the preset mission, and select the drones to be assembled; The drones to be grouped are sorted according to the effective performance evaluation index to obtain the optimal effective performance evaluation index, and the corresponding drone is used as the leader of the drone cluster grouping, and the leader is positioned at the center of the drone cluster grouping graphic; The drones to be grouped complete the drone cluster formation according to the instructions of the leader.

2. The method for forming a swarm of drones according to claim 1, characterized in that: The step of obtaining the performance requirement information of the drone cluster formation, extracting the performance requirement data according to the performance requirement information, and processing the performance requirement data to obtain the performance requirement index specifically includes: Obtain the performance requirement information of the drone cluster formation and extract the performance requirement data, including the total operation time requirement data, the positioning accuracy requirement data, the communication stability requirement data and the processor performance requirement data; The total operation time requirement data, positioning accuracy requirement data, communication stability requirement data and processor performance requirement data are input into a preset performance requirement index evaluation model for processing to obtain a performance requirement index.

3. The method for forming a swarm of drones according to claim 2, characterized in that: The obtaining of the performance attribute data of the drone, processing the performance attribute data to obtain a performance evaluation index, and comparing the performance evaluation index with the performance requirement index to obtain an effective performance evaluation index specifically includes: Obtain the performance attribute data of the drone, including total running time data, historical positioning accuracy data, historical communication stability data, and configured processor performance data; Inputting the total running time data, historical positioning accuracy data, historical communication stability data, and configuration processor performance data into a preset performance index evaluation model for processing to obtain a performance evaluation index; Comparing the performance evaluation index with the performance requirement index to obtain the performance matching status of the UAV, including a valid performance evaluation index or an invalid performance evaluation index; If the performance evaluation index is greater than the performance requirement index, the performance matching state of the UAV is a valid performance evaluation index.

4. The method for forming a swarm of drones according to claim 3, characterized in that: The obtaining of the number of drones corresponding to the effective performance evaluation index and comparing it with the number of drones required by the preset task to select the drones to be grouped specifically includes: The effective performance evaluation index is counted to obtain the corresponding number of available drones; Compare the number of available drones with the number of drones required for a preset mission by a threshold value to obtain a flight readiness state; If the number of available drones is greater than or equal to the number of drones required for the mission, the flight preparation state is a state that meets the flight conditions; If the number of available drones is less than the number of drones required for the mission, the flight preparation state is a state where the flight conditions are not met; If the flight preparation state satisfies the flight conditions, a drone to be assembled is selected from the available drones.

5. The method for forming a swarm of drones according to claim 4, characterized in that: The method of sorting the drones to be grouped according to the effective performance evaluation index to obtain the optimal effective performance evaluation index, and using the corresponding drone as the leader of the drone cluster grouping, and positioning the leader at the center of the drone cluster grouping graphic, specifically includes: Sorting the drones to be organized in descending order according to the effective performance evaluation index to obtain a sequential effective performance evaluation index; Selecting the UAV corresponding to the maximum effective performance evaluation index in the sequential effective performance evaluation index as the leader of the UAV cluster formation; Obtain the preset grouping information of the drone cluster grouping and extract the graphic center of the grouping layout; Position the leader in the center of the graph.

6. The method for forming a swarm of drones according to claim 5, characterized in that: Also includes: Obtain real-time operation data of each drone in the drone cluster, including real-time range data, real-time communication stability data, and real-time positioning accuracy data; Inputting the real-time cruising range data, the real-time communication stability data, and the real-time positioning accuracy data into a preset real-time operation performance evaluation model for processing to obtain a real-time operation performance index; Sorting the real-time operation performance indexes in descending order to obtain the maximum real-time operation performance index; The unattended node corresponding to the maximum real-time operation performance index is used as the real-time leader, and the real-time leader is swapped with the leader.

7. A drone clustering system, characterized in that: The method comprises a memory and a processor, wherein the memory comprises a drone cluster grouping method program, and when the drone cluster grouping method program is executed by the processor, the following steps are implemented: Obtain the performance requirement information of the UAV cluster formation, extract the performance requirement data according to the performance requirement information, and process it to obtain the performance requirement index; Acquiring performance attribute data of the drone, processing the performance attribute data to obtain a performance evaluation index, and comparing the performance evaluation index with the performance requirement index to obtain an effective performance evaluation index; Obtain the number of drones corresponding to the effective performance evaluation index, compare it with the number of drones required for the preset mission, and select the drones to be assembled; The drones to be grouped are sorted according to the effective performance evaluation index to obtain the optimal effective performance evaluation index, and the corresponding drone is used as the leader of the drone cluster grouping, and the leader is positioned at the center of the drone cluster grouping graphic; The drones to be grouped complete the drone cluster formation according to the instructions of the leader.

8. The drone swarm grouping system according to claim 7, characterized in that: The step of obtaining the performance requirement information of the drone cluster formation, extracting the performance requirement data according to the performance requirement information, and processing the performance requirement data to obtain the performance requirement index specifically includes: Obtain the performance requirement information of the drone cluster formation and extract the performance requirement data, including the total operation time requirement data, the positioning accuracy requirement data, the communication stability requirement data and the processor performance requirement data; The total operation time requirement data, positioning accuracy requirement data, communication stability requirement data and processor performance requirement data are input into a preset performance requirement index evaluation model for processing to obtain a performance requirement index.

9. The drone swarm grouping system according to claim 8, characterized in that: The obtaining of the performance attribute data of the drone, processing the performance attribute data to obtain a performance evaluation index, and comparing the performance evaluation index with the performance requirement index to obtain an effective performance evaluation index specifically includes: Obtain the performance attribute data of the drone, including total running time data, historical positioning accuracy data, historical communication stability data, and configured processor performance data; Inputting the total running time data, historical positioning accuracy data, historical communication stability data, and configuration processor performance data into a preset performance index evaluation model for processing to obtain a performance evaluation index; Comparing the performance evaluation index with the performance requirement index to obtain the performance matching status of the UAV, including a valid performance evaluation index or an invalid performance evaluation index; If the performance evaluation index is greater than the performance requirement index, the performance matching state of the UAV is a valid performance evaluation index.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a drone cluster grouping method program, and when the drone cluster grouping method program is executed by a processor, the steps of the drone cluster grouping method as described in any one of claims 1 to 6 are implemented.

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