WO2024114543A1 - 一种无人驾驶航空器的权限管控方法、系统和存储介质 - Google Patents

一种无人驾驶航空器的权限管控方法、系统和存储介质 Download PDF

Info

Publication number
WO2024114543A1
WO2024114543A1 PCT/CN2023/134145 CN2023134145W WO2024114543A1 WO 2024114543 A1 WO2024114543 A1 WO 2024114543A1 CN 2023134145 W CN2023134145 W CN 2023134145W WO 2024114543 A1 WO2024114543 A1 WO 2024114543A1
Authority
WO
WIPO (PCT)
Prior art keywords
unmanned aerial
fleet
instruction
data
mission
Prior art date
Application number
PCT/CN2023/134145
Other languages
English (en)
French (fr)
Inventor
胡华智
薛鹏
陈皓东
Original Assignee
亿航智能设备（广州）有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 亿航智能设备（广州）有限公司 filed Critical 亿航智能设备（广州）有限公司
Publication of WO2024114543A1 publication Critical patent/WO2024114543A1/zh

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2457—Query processing with adaptation to user needs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the present invention relates to the technical field of unmanned aerial vehicle management, and in particular to an unmanned aerial vehicle authority management method, system and storage medium.
  • unmanned aerial vehicles are widely used. They can enhance the transportation capacity in remote areas, improve agricultural production efficiency, and solve urban logistics and urban planning and construction management. They have universal significance. With the further increase and expansion of the types and uses of unmanned aerial vehicles, the safety management issues of unmanned aerial vehicles have become increasingly prominent.
  • the purpose of the embodiments of the present invention is to provide an authority management method, system and storage medium for unmanned aerial vehicles, which can obtain task instruction bars based on the fleet instruction code combined with the obtained instruction task data to manage and dispatch the fleet and correct the management level of matching authority, thereby improving the intelligent and precise management and control of the unmanned aerial vehicle task instruction authority.
  • the embodiment of the present invention also provides a method for controlling the authority of an unmanned aerial vehicle, comprising the following steps:
  • the unmanned aerial vehicles in the target area are numbered to obtain the fleet instruction code
  • the step of numbering unmanned aerial vehicles in a target area based on mission operation information to obtain a fleet instruction code includes:
  • the mission factor is extracted to obtain the fleet size level of unmanned aerial vehicles required to perform the mission in the target area;
  • the unmanned aerial vehicles in the fleet are numbered according to the unmanned aerial vehicle fleet size level and expected value and the mission code to obtain a fleet instruction code.
  • the step of extracting the operation data information of the task operation information and inputting it into a preset task instruction database model to obtain the instruction task data of the unmanned aerial vehicle includes:
  • Acquire operation data information of the task operation information including operation content data, operation volume data, operation scope data, and operation level data;
  • operation content data operation volume data, operation scope data, and operation level data, input into a preset task instruction database model to obtain instruction task data;
  • the mission instruction database model is trained by inputting historical unmanned aerial vehicle operation content data, operation volume data, operation scope data, and operation level data into an initial mission instruction database model.
  • the step of obtaining the unmanned aerial vehicle task instruction bar according to the instruction task data combined with the fleet instruction code and managing the dispatching fleet includes:
  • the sub-command mission data corresponds to route data, destination data, mission node data, and activity area data of the unmanned aerial vehicles in the fleet;
  • the unmanned aerial vehicles in the fleet are dispatched and controlled according to the mission instruction bar.
  • the task status information of the unmanned aerial vehicles in the fleet obtained through real-time monitoring is combined with the instruction task data to perform synchronous fitting to obtain permission matching management, including:
  • the mission status information includes dynamic route data, heading target data, real-time mission data and dynamic flight area data of the unmanned aerial vehicle;
  • Unmanned aerial vehicles that do not meet the threshold comparison requirements will be marked and the fleet instruction codes will be modified and the authority will be revised.
  • the permission control method for an unmanned aerial vehicle further includes:
  • the mission instruction bar of the unmanned aerial vehicle is formatted, and the fleet instruction code is synchronously corrected.
  • the permission control method for an unmanned aerial vehicle further includes:
  • the unmanned aerial vehicles in the fleet are displayed in grades according to the instruction levels and the fleet operation status information.
  • an embodiment of the present invention provides an authority control system for an unmanned aerial vehicle, the system comprising: a memory and a processor, the memory comprising a program of an authority control method for an unmanned aerial vehicle, and when the program of the authority control method for an unmanned aerial vehicle is executed by the processor, the following steps are implemented:
  • the unmanned aerial vehicles in the target area are numbered to obtain the fleet instruction code
  • the step of numbering the unmanned aerial vehicles in the target area based on the mission operation information to obtain the fleet instruction code includes:
  • the mission factor is extracted to obtain the fleet size level of unmanned aerial vehicles required to perform the mission in the target area;
  • an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium includes an unmanned aerial vehicle authority control method program, and when the unmanned aerial vehicle authority control method program is executed by a processor, the steps of the unmanned aerial vehicle authority control method as described in any one of the above items are implemented.
  • an unmanned aerial vehicle authority management method, system and storage medium obtains fleet instruction codes by numbering unmanned aerial vehicles in a target area based on mission operation information, extracts operation data information of mission operation information and inputs it into a preset mission instruction database model to obtain instruction task data of the unmanned aerial vehicle, and obtains mission instruction bars in combination with the fleet instruction codes and manages and dispatches the fleet, and synchronizes and fits the mission status information of the unmanned aerial vehicle obtained through real-time monitoring with the instruction task data to obtain authority matching management; thereby, task instruction bars are obtained based on the combination of the fleet instruction codes and the obtained instruction task data to manage and dispatch the fleet, and the authority matching management is corrected through the synchronized fitting of the mission status information and the instruction task data, thereby improving the intelligent and precise management and control of the unmanned aerial vehicle mission instruction authorities.
  • FIG1 is a flow chart of a method for controlling the authority of an unmanned aerial vehicle provided by an embodiment of the present invention
  • FIG2 is a flow chart of generating fleet instruction codes of the method for controlling the authority of unmanned aerial vehicles provided by an embodiment of the present invention
  • FIG. 3 is a diagram of an unmanned aerial vehicle authority control method provided by an embodiment of the present invention. Flowchart of command mission data for manned aircraft;
  • FIG. 4 is a schematic diagram of the structure of an authority management and control system for an unmanned aerial vehicle provided in an embodiment of the present invention.
  • FIG. 1 is a flow chart of a method for controlling the authority of an unmanned aerial vehicle in some embodiments of the present invention.
  • the method for controlling the authority of an unmanned aerial vehicle is used in a terminal device, such as a computer, a control terminal, etc.
  • the method for controlling the authority of an unmanned aerial vehicle comprises the following steps:
  • S104 Perform synchronous fitting based on the mission status information of the unmanned aerial vehicle in the fleet acquired through real-time monitoring and combined with the instruction mission data to obtain authority matching management.
  • the unmanned aerial vehicles in the target area are numbered according to the mission operation information to obtain the fleet instruction code
  • the operation data information of the mission operation information is extracted and input into the preset mission instruction database model to obtain the instruction mission data of the unmanned aerial vehicle
  • the unmanned aerial vehicle mission instruction bar is obtained according to the instruction mission data combined with the fleet instruction code, and the fleet is managed and dispatched, and it also includes obtaining the mission status information of the fleet unmanned aerial vehicles based on real-time monitoring and synchronously fitting with the instruction mission data to obtain the authority matching management to perform status supervision and authority regulation of the unmanned aerial vehicles.
  • FIG. 2 is a flow chart of generating fleet instruction codes of the method for controlling the authority of unmanned aerial vehicles in some embodiments of the present invention.
  • the fleet instruction codes are obtained by numbering the unmanned aerial vehicles in the target area based on the mission operation information, specifically:
  • S203 numbering the unmanned aerial vehicles in the fleet according to the unmanned aerial vehicle fleet size level and expected value and the mission code to obtain a fleet instruction code.
  • the task factor is extracted according to the preset task operation information, and the scale level of the unmanned aerial vehicle fleet required to perform the task in the target area is obtained according to the task factor. Due to the differences in tasks, workloads, coverage areas, and workload levels, the scale of the unmanned aerial vehicle fleet required to perform tasks in a certain target area is also different. Therefore, according to the extracted task factor, the corresponding fleet scale level can be obtained to meet the task requirements, and then the level threshold matching is performed according to the fleet scale level to obtain the expected value of the unmanned aerial vehicle model and quantity corresponding to the level threshold.
  • the level threshold is divided into seven levels, and the threshold ranges are [0, 0.21], ( 0.21, 0.38], (0.38, 0.51], (0.51, 0.69], (0.69, 0.8], (0.8, 0.93], (0.93, 1.0], different levels correspond to different models and quantities.
  • the fleet size level of fleet A corresponds to the level threshold range of (0.51, 0.69], corresponding to the fourth level
  • the preset models and quantities of the fourth level unmanned aerial vehicles are 2 B-type, 4 M-type, 6 S-type, and 3 Y-type
  • the model and quantity expected values of fleet A are B2, M4, S6, and Y3.
  • the unmanned aerial vehicles in the fleet are assigned The piloted aircraft is numbered to obtain the fleet instruction code.
  • the fleet instruction code of the second B-type unmanned aircraft in fleet A is P4K6-IV-B2M4S6Y3-B2
  • the fleet instruction code is P4K6-IV-B2M4S6Y3, where P4K6 is the mission code.
  • the fleet size level calculation formula is:
  • En is the fleet size level
  • S0 is the target area size value
  • ⁇ is the mission factor
  • ⁇ is the task special coefficient
  • Figure 3 is a flow chart of obtaining command task data of an unmanned aerial vehicle in a method for controlling the authority of an unmanned aerial vehicle in some embodiments of the present invention.
  • the operation data information of the task operation information is extracted and input into a preset task instruction database model to obtain the command task data of the unmanned aerial vehicle, specifically:
  • operation data information of the task operation information including operation content data, operation volume data, operation scope data, and operation level data;
  • the mission instruction database model is trained by inputting historical unmanned aerial vehicle operation content data, operation volume data, operation scope data, and operation level data into an initial mission instruction database model.
  • the operation data information is extracted according to the task job information and input into the preset task instruction database model to output the instruction task data.
  • the task instruction database model requires a large amount of historical data for training. The larger the amount of data, the more accurate the result.
  • the preset task instruction database model in this scheme is trained with historical operation data information and instruction task data as input. A large amount of test data is compared with real data to improve the accuracy of the results.
  • the accuracy threshold is set to 95%.
  • the step of obtaining the unmanned aerial vehicle mission instruction bar and managing the dispatching fleet according to the instruction mission data combined with the fleet instruction code is specifically as follows:
  • the sub-command mission data corresponds to the route data, destination, data, task node data, and activity area data;
  • the unmanned aerial vehicles in the fleet are dispatched and controlled according to the mission instruction bar.
  • the sub-command task data corresponding to each unmanned aerial vehicle in the fleet is extracted in the task data packet, and then the sub-command task data is marked with commands according to the fleet command code of each unmanned aerial vehicle to generate a task command bar.
  • Each command task bar corresponds to the data information of the command task of each unmanned aerial vehicle in the fleet.
  • commands are sent to the corresponding unmanned aerial vehicles in the fleet, information is exchanged, and scheduling control is performed to realize customized regulation of each unmanned aerial vehicle in the fleet.
  • the task status information of the unmanned aerial vehicle in the fleet obtained through real-time monitoring is combined with the instruction task data to perform synchronous fitting to obtain authority matching management, specifically:
  • the mission status information includes dynamic route data, heading target data, real-time mission data and dynamic flight area data of the unmanned aerial vehicle;
  • Unmanned aerial vehicles that do not meet the threshold comparison requirements will be marked and the fleet instruction codes will be modified and the authority will be revised.
  • a similarity fitting calculation is performed based on the real-time mission status information of the unmanned aerial vehicle, including dynamic route data, heading target data, real-time mission data, and dynamic flight area data, and the route data, destination data, mission node data, and activity area data corresponding to the sub-command mission data corresponding to the number of the unmanned aerial vehicle.
  • the similarity fitting calculation result is the synchronization fitting calculation result.
  • the similarity fitting calculation adopts Euclidean distance similarity or cosine similarity to obtain the mission similarity, and then compares the preset command threshold with the corresponding unmanned aerial vehicle in the fleet to determine whether the mission execution status of the currently monitored unmanned aerial vehicle meets the preset command requirements.
  • the unmanned aerial vehicle is marked and the fleet command coding is modified and the authority is revised, or a patch is added to encode and command deployment of other backup unmanned aerial vehicles.
  • it also includes:
  • the mission instruction bar of the unmanned aerial vehicle is formatted, and the fleet instruction code is synchronously corrected.
  • the unmanned aerial vehicle executes the mission instruction, it is necessary to conduct a simulated self-inspection of the energy information and status information of each unmanned aerial vehicle to verify whether it meets the mission requirements. For example, only unmanned aerial vehicles with energy levels not less than 75% of the endurance information and endurance status levels not less than 80% of the route information are allowed to execute the mission. If the above two indicators cannot be met at the same time, the unmanned aerial vehicle needs to be formatted and the fleet instruction code needs to be corrected synchronously, and other backup unmanned aerial vehicles need to be added to fill the blanks.
  • it also includes:
  • the unmanned aerial vehicles in the fleet are displayed in grades according to the instruction levels and the fleet operation status information.
  • the mission instructions executed by each unmanned aerial vehicle in the fleet often contain multiple mission instruction bars, and different instruction sub-tasks are executed according to different instruction bars.
  • the instruction level is extracted according to the mission factor combined with the mission instruction bar, and the instructions of each unmanned aerial vehicle in the fleet corresponding to the instruction level are displayed in a graded manner according to the instruction level and the fleet operation status information to clarify the priority order of the instructions, facilitate the grasp of the mission status under the real-time instructions, and can adjust the mission instruction bar at any time to control the allocation of mission instructions.
  • it also includes:
  • the unmanned aerial vehicle task scheduling database includes the task level and fleet quantity of each type of unmanned aerial vehicle in different historical tasks and the corresponding historical tasks and emergency response level under the task plan;
  • Performing a similarity comparison in the unmanned aerial vehicle task scheduling database according to the type, mission level and fleet number of the unmanned aerial vehicle to be dispatched obtaining the unmanned aerial vehicle historical tasks whose similarity with the type, mission level and fleet number of the unmanned aerial vehicle to be dispatched meets the preset value requirements as the target tasks of the unmanned aerial vehicle to be dispatched;
  • the target mission is modified according to the emergency response level, the modified mission to be dispatched by the unmanned aerial vehicle is formulated, and the unmanned aerial vehicle is dispatched to perform the mission.
  • the tasks and emergency situations of different types of unmanned aerial vehicles under different dispatch missions, dispatch numbers and working conditions of mission plans are Measures include statistically establishing an unmanned aerial vehicle task scheduling database, which contains the task status, dispatched numbers, and corresponding tasks and emergency risk response levels of various types of unmanned aerial vehicles in historical missions. This allows for easy comparison based on the historical database to obtain the best tasks and task risk status for the unmanned aerial vehicles to be dispatched, and to modify task instructions based on the risks of the tasks, so as to reduce mission risks and unmanned aerial vehicle losses.
  • it also includes:
  • the preset threshold value of the unmanned aerial vehicle state is divided into a threshold range according to the working state of the type of unmanned aerial vehicle in performing the mission;
  • the preset thresholds of the unmanned aerial vehicle status are divided into three levels;
  • the unmanned aerial vehicle continues to perform the mission
  • the working task of the unmanned aerial vehicle is terminated and recalled.
  • the real-time status information of each type of unmanned aerial vehicle when performing a task its working status is formulated and divided into threshold ranges of different levels, and the threshold monitoring of the real-time working status information parameters of the unmanned aerial vehicle is performed according to the formulated threshold range.
  • the command of the unmanned aerial vehicle is corrected according to the preset threshold range.
  • the preset threshold range of the unmanned aerial vehicle state is divided into: [0, 0.5), [0.5, 0.8), [0.8, 1.0].
  • the task continues to be performed, and when it is within the second preset threshold range, the task continues to be performed.
  • the threshold range the working state of the unmanned aerial vehicle is interrupted and changed to the on-site standby state waiting for the unmanned aerial vehicle to be repaired.
  • the working task of the unmanned aerial vehicle is terminated and recalled, wherein the real-time working state parameters of the unmanned aerial vehicle are obtained based on the real-time whole machine state information and operation trajectory data information of the dispatched unmanned aerial vehicle.
  • the present invention further discloses an authority control system for an unmanned aerial vehicle, including a memory 41 and a processor 42.
  • the memory includes an authority control method program for an unmanned aerial vehicle.
  • the authority control method program for an unmanned aerial vehicle is executed by the processor, the following steps are implemented:
  • the unmanned aerial vehicles in the target area are numbered to obtain the fleet instruction code
  • the unmanned aerial vehicles in the target area are numbered according to the mission operation information to obtain the fleet instruction code
  • the operation data information of the mission operation information is extracted and input into the preset mission instruction database model to obtain the instruction mission data of the unmanned aerial vehicle
  • the unmanned aerial vehicle mission instruction bar is obtained according to the instruction mission data combined with the fleet instruction code, and the fleet is managed and dispatched, and it also includes obtaining the mission status information of the fleet unmanned aerial vehicles based on real-time monitoring and synchronously fitting with the instruction mission data to obtain the authority matching management to perform status supervision and authority regulation of the unmanned aerial vehicles.
  • the unmanned aerial vehicles in the target area are numbered based on the mission operation information to obtain the fleet instruction code, specifically:
  • the mission factor is extracted to obtain the fleet size level of unmanned aerial vehicles required to perform the mission in the target area;
  • the level threshold matching is performed to obtain the unmanned aerial vehicle model and Quantity expectation
  • the unmanned aerial vehicles in the fleet are numbered according to the unmanned aerial vehicle fleet size level and expected value and the mission code to obtain a fleet instruction code.
  • the task factor is extracted according to the preset task operation information, and the size level of the unmanned aerial vehicle fleet required to perform the task in the target area is obtained according to the task factor. Due to the differences in tasks, workloads, coverage areas, and workload levels, the size of the unmanned aerial vehicle fleet required to perform tasks in a certain target area is also different. Therefore, according to the extracted task factor, the corresponding fleet size level can be obtained to meet the task requirements, and then the level threshold matching is performed according to the fleet size level to obtain the expected value of the unmanned aerial vehicle model and quantity corresponding to the level threshold.
  • the level threshold is divided into seven levels, and the threshold ranges are [0, 0.21], (0.21, 0.38], (0.38, 0.51], (0.51, 0.69], (0.69, 0.8], (0.8, 0.93], (0.93, 1.0], different levels correspond to different models and quantities.
  • the fleet size level of fleet A corresponds to the level threshold range of (0.51, 0.69], corresponding to the fourth level
  • the preset models and quantities of the fourth-level unmanned aerial vehicles are 2 B-type, 4 M-type, 6 S-type, and 3 Y-type, respectively
  • the model and quantity expected values of fleet A are B2, M4, S6, and Y3.
  • the unmanned aerial vehicles in the fleet are numbered to obtain the fleet instruction code.
  • the fleet instruction code of the second B-type unmanned aerial vehicle in fleet A is P4K6-IV-B2M4S6Y3-B2
  • the fleet instruction code is P4K6-IV-B2M4S6Y3, where P4K6 is the mission code
  • the fleet size level calculation formula is:
  • En is the fleet size level
  • S0 is the target area size value
  • ⁇ is the mission factor
  • ⁇ is the task special coefficient
  • the extracting the operation data information of the task operation information and inputting it into a preset task instruction database model to obtain the instruction task data of the unmanned aerial vehicle is specifically as follows:
  • Acquire operation data information of the task operation information including operation content data, operation volume data, operation scope data, and operation level data;
  • operation content data Obtain instruction task data by inputting into a preset task instruction database model
  • the mission instruction database model is trained by inputting historical unmanned aerial vehicle operation content data, operation volume data, operation scope data, and operation level data into an initial mission instruction database model.
  • the operation data information is extracted according to the task job information and input into the preset task instruction database model to output the instruction task data.
  • the task instruction database model requires a large amount of historical data for training. The larger the amount of data, the more accurate the result.
  • the preset task instruction database model in this scheme is trained with historical operation data information and instruction task data as input. A large amount of test data is compared with real data to improve the accuracy of the results obtained.
  • the accuracy threshold is set to 95%.
  • the step of obtaining the unmanned aerial vehicle mission instruction bar and managing the dispatching fleet according to the instruction mission data combined with the fleet instruction code is specifically as follows:
  • the sub-command mission data corresponds to route data, destination data, mission node data, and activity area data of the unmanned aerial vehicles in the fleet;
  • the unmanned aerial vehicles in the fleet are dispatched and controlled according to the mission instruction bar.
  • the sub-command task data corresponding to each unmanned aerial vehicle in the fleet is extracted in the task data packet, and then the sub-command task data is marked with commands according to the fleet command code of each unmanned aerial vehicle to generate a task command bar.
  • Each command task bar corresponds to the data information of the command task of each unmanned aerial vehicle in the fleet.
  • commands are sent to the corresponding unmanned aerial vehicles in the fleet, information is exchanged, and scheduling control is performed to realize customized regulation of each unmanned aerial vehicle in the fleet.
  • the task status information of the unmanned aerial vehicle in the fleet obtained through real-time monitoring is combined with the instruction task data to perform synchronous fitting to obtain authority matching management, specifically:
  • the mission status information includes dynamic route data, heading target data, real-time mission data and dynamic flight area data of the unmanned aerial vehicle;
  • Unmanned aerial vehicles that do not meet the threshold comparison requirements will be marked and the fleet instruction codes will be modified and the authority will be revised.
  • the similarity fitting calculation is performed on the data, task node data and activity area data.
  • the similarity fitting calculation result is the synchronization fitting calculation result.
  • the similarity fitting calculation adopts Euclidean distance similarity or cosine similarity to obtain task similarity, and then compares the preset instruction threshold with the corresponding unmanned aerial vehicle in the fleet to determine whether the task execution status of the currently monitored unmanned aerial vehicle meets the preset instruction requirements.
  • the unmanned aerial vehicle is marked and the fleet instruction coding is modified and the authority is revised, or a patch is added to encode and command deployment of other backup unmanned aerial vehicles.
  • it also includes:
  • the mission instruction bar of the unmanned aerial vehicle is formatted, and the fleet instruction code is synchronously corrected.
  • the unmanned aerial vehicle executes the mission instruction, it is necessary to conduct a simulated self-inspection of the energy information and status information of each unmanned aerial vehicle to verify whether it meets the mission requirements. For example, only unmanned aerial vehicles with energy levels not less than 75% of the endurance information and endurance status levels not less than 80% of the route information are allowed to execute the mission. If the above two indicators cannot be met at the same time, the unmanned aerial vehicle needs to be formatted and the fleet instruction code needs to be corrected synchronously, and other backup unmanned aerial vehicles need to be added to fill the blanks.
  • it also includes:
  • the unmanned aerial vehicles in the fleet are displayed in grades according to the instruction levels and the fleet operation status information.
  • the mission instructions executed by each unmanned aerial vehicle in the fleet often contain multiple mission instruction bars, and different instruction sub-tasks are executed according to different instruction bars.
  • the instruction level is extracted according to the mission factor combined with the mission instruction bar, and the instructions of each unmanned aerial vehicle in the fleet corresponding to the instruction level are displayed in a graded manner according to the instruction level and the fleet operation status information to clarify the instruction priority order, facilitate the grasp of the task status under real-time instructions, and can adjust the task instruction bar at any time to control the task instruction allocation.
  • it also includes:
  • the unmanned aerial vehicle task scheduling database includes the task levels and fleet numbers of various types of unmanned aerial vehicles in different historical tasks, as well as the corresponding historical tasks and response plans under the task plans.
  • Emergency response level ;
  • Performing a similarity comparison in the unmanned aerial vehicle task scheduling database according to the type, mission level and fleet number of the unmanned aerial vehicle to be dispatched obtaining the unmanned aerial vehicle historical tasks whose similarity with the type, mission level and fleet number of the unmanned aerial vehicle to be dispatched meets the preset value requirements as the target tasks of the unmanned aerial vehicle to be dispatched;
  • the target mission is modified according to the emergency response level, the modified mission to be dispatched by the unmanned aerial vehicle is formulated, and the unmanned aerial vehicle is dispatched to perform the mission.
  • an unmanned aerial vehicle task scheduling database is established based on statistics of tasks and emergency measures for different types of unmanned aerial vehicles under different dispatch missions, dispatch numbers and working conditions of mission plans.
  • This database contains the mission status, dispatch numbers and corresponding tasks and emergency risk response levels of various types of unmanned aerial vehicles in historical missions, which can facilitate comparison based on the historical database to obtain the best mission and mission risk situation for the unmanned aerial vehicles to be dispatched, and to modify the mission instructions based on the mission risk, so as to reduce mission risks and unmanned aerial vehicle losses.
  • it also includes:
  • the preset threshold value of the unmanned aerial vehicle state is divided into a threshold range according to the working state of the type of unmanned aerial vehicle in performing the mission;
  • the preset thresholds of the unmanned aerial vehicle status are divided into three levels;
  • the unmanned aerial vehicle continues to perform the mission
  • the threshold of the real-time working state parameter of the unmanned aerial vehicle is within the second preset threshold range of the unmanned aerial vehicle state, interrupting the working state of the unmanned aerial vehicle and changing it to the standby state, waiting for the unmanned aerial vehicle to be inspected and inspected;
  • the working task of the unmanned aerial vehicle is terminated and recalled.
  • the real-time status information of each type of unmanned aerial vehicle when performing a task its working status is formulated and divided into threshold ranges of different levels, and the real-time working status information parameters of the unmanned aerial vehicle are threshold-monitored according to the formulated threshold range.
  • the command of the unmanned aerial vehicle is corrected according to the preset threshold range.
  • the preset threshold range of the unmanned aerial vehicle status is divided into: [0, 0.5), [0.5, 0.8), [0.8, 1.0].
  • the threshold of the real-time working status parameter of the unmanned aerial vehicle is in the first preset threshold range, the task continues to be performed.
  • the working status of the unmanned aerial vehicle is interrupted and changed to the standby status on the spot waiting for the unmanned aerial vehicle to be repaired.
  • the working task of the unmanned aerial vehicle is terminated and recalled.
  • the real-time working status parameters of the unmanned aerial vehicle are obtained according to the real-time whole machine status information and operation trajectory data information of the dispatched unmanned aerial vehicle.
  • a third aspect of the present invention provides a readable storage medium, which includes an unmanned aerial vehicle authority control method program.
  • the unmanned aerial vehicle authority control method program is executed by a processor, the steps of the unmanned aerial vehicle authority control method as described in any one of the above items are implemented.
  • the present invention discloses a method, system and storage medium for controlling the authority of unmanned aerial vehicles.
  • the method obtains fleet instruction codes by numbering unmanned aerial vehicles in a target area based on mission operation information, extracts operation data information of mission operation information, and inputs the operation data information into a preset mission instruction database model to obtain the instruction mission data of the unmanned aerial vehicle, obtains the mission instruction bar in combination with the fleet instruction code, manages and dispatches the fleet, and obtains authority matching management by synchronously fitting the mission status information of the unmanned aerial vehicle obtained by real-time monitoring with the instruction mission data; thereby, based on the fleet instruction code,
  • the code is combined with the obtained command task data to obtain the task instruction bar to manage the dispatching fleet and correct the authority matching management through the synchronous fitting of the task status information and the command task data, so as to improve the intelligent and precise management and control of the unmanned aerial vehicle task instruction authority.
  • the disclosed devices and methods can be implemented in other ways.
  • the device embodiments described above are only schematic.
  • the division of the units is only a logical function division.
  • 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.
  • 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.
  • 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.
  • the 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.
  • the technical solution of the embodiment of the present invention, or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium. It includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in various embodiments of the present invention.
  • the aforementioned storage medium includes: a mobile storage device, ROM, RAM, a magnetic disk or an optical disk, etc., which can store program codes.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Software Systems (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Data Mining & Analysis (AREA)
  • Strategic Management (AREA)
  • General Business, Economics & Management (AREA)
  • Medical Informatics (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Marketing (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Game Theory and Decision Science (AREA)
  • Evolutionary Computation (AREA)
  • Tourism & Hospitality (AREA)
  • Educational Administration (AREA)
  • Computing Systems (AREA)
  • Development Economics (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Databases & Information Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Traffic Control Systems (AREA)

Abstract

一种无人驾驶航空器的权限管控方法、系统和存储介质。该方法包括：基于任务作业信息对目标区域内无人驾驶航空器进行编号得到机队指令编码提取任务作业信息的作业数据信息并输入预设任务指令数据库模型中获取无人驾驶航空器的指令任务数据，并结合机队指令编码得到任务指令条并管理调度机队，根据实时监测获取的无人驾驶航空器的任务状态信息与指令任务数据进行同步拟合获得权限匹配管理；从而基于机队指令编码与获得的指令任务数据相结合获得任务指令条管理调度机队并通过任务状态信息与指令任务数据的同步拟合进行权限匹配管理的修正。

Description

一种无人驾驶航空器的权限管控方法、系统和存储介质 技术领域
本发明涉及无人驾驶航空器管理技术领域，具体而言，涉及一种无人驾驶航空器的权限管控方法、系统和存储介质。
背景技术
目前无人驾驶航空器的应用广泛，可提升偏远地区的运输能力以及提高农业生产效率以及解决城市物流和城市规划建设管理具有普遍性意义，且随着无人驾驶航空器类型和用途的进一步增加增广，针对无人驾驶航空器的安全管理问题越发凸显。
目前无人驾驶航空器机群、机队协同作业的应用领域如勘探、农业播种等，由于存在机队系统管理调度维度的科学性短板，导致传统终端操控存在控制差错或错乱，程序式操控又易出现权限错乱和灵活机动性不佳的缺陷，因此对于无人驾驶航空器的多机协同权限管控的智能化技术是亟待解决的。
针对上述问题，目前亟待有效的技术解决方案。
发明内容
本发明实施例的目的在于提供无人驾驶航空器的权限管控方法、系统和存储介质，可以根据机队指令编码与获得的指令任务数据相结合获得任务指令条管理调度机队并修正匹配权限的管理级数，提高对无人驾驶航空器任务指令权限的智能化精准管理调控。
本发明实施例还提供了无人驾驶航空器的权限管控方法，包括以下步骤：
基于任务作业信息对目标区域内无人驾驶航空器进行编号得到机队指令编码；
提取所述任务作业信息的作业数据信息并输入预设任务指令数据库模型中获取无人驾驶航空器的指令任务数据；
根据所述指令任务数据结合机队指令编码得到无人驾驶航空器任务指令条并管理调度机队；
根据实时监测获取的机队所述无人驾驶航空器的任务状态信息，结合所述指令任务数据进行同步拟合获得权限匹配管理。
可选地，在本发明实施例所述的无人驾驶航空器的权限管控方法中，所述基于任务作业信息对目标区域内无人驾驶航空器进行编号得到机队指令编码，包括：
基于预设的任务作业信息提取任务因子得到目标区域内所需执行任务的无人驾驶航空器机队规模等级；
根据所述机队规模等级进行等级阈值匹配获得无人驾驶航空器型号和数量期望值；
根据所述无人驾驶航空器机队规模等级和期望值以及任务代码对机队中的无人驾驶航空器进行编号得到机队指令编码。
可选地，在本发明实施例所述的无人驾驶航空器的权限管控方法中，所述提取所述任务作业信息的作业数据信息并输入预设任务指令数据库模型中获取无人驾驶航空器的指令任务数据，包括：
获取所述任务作业信息的作业数据信息，包括作业内容数据、作业体量数据、作业范围数据、作业级别数据；
根据所述作业内容数据、作业体量数据、作业范围数据、作业级别数据输入至预设的任务指令数据库模型中获得指令任务数据；
所述任务指令数据库模型根据历史无人驾驶航空器的作业内容数据、作业体量数据、作业范围数据、作业级别数据输入至初始任务指令数据库模型中训练获得。
可选地，在本发明实施例所述的无人驾驶航空器的权限管控方法中，所述根据所述指令任务数据结合机队指令编码得到无人驾驶航空器任务指令条并管理调度机队，包括：
根据所述指令任务数据提取子指令任务数据；
所述子指令任务数据对应机队中无人驾驶航空器的航路数据、目的地数据、任务节点数据以及活动区域数据；
根据所述机队指令编码对所述无人驾驶航空器的子指令任务数据进行指令标注并生成任务指令条；
根据所述任务指令条对所述机队中各无人驾驶航空器进行调度控制。
可选地，在本发明实施例所述的无人驾驶航空器的权限管控方法中，所述根据实时监测获取的机队所述无人驾驶航空器的任务状态信息，结合所述指令任务数据进行同步拟合获得权限匹配管理，包括：
监测并提取机队所述无人驾驶航空器的实时任务状态信息；
所述任务状态信息包括所述无人驾驶航空器的动态航线数据、航向目标数据、实时任务数据以及动态飞行区域数据；
根据所述动态航线数据、航向目标数据、实时任务数据以及动态飞行区域数据与所述无人驾驶航空器的编号对应的子指令任务数据进行同步拟合度计算获得任务相似度；
根据所述任务相似度对所述机队中各无人驾驶航空器进行指令阈值对比；
将不符合阈值对比要求的无人驾驶航空器进行标注并对机队指令编码进行修改和权限修订。
可选地，在本发明实施例所述的无人驾驶航空器的权限管控方法中，还包括：
对所述机队中各无人驾驶航空器进行模拟自检；
获取所述无人驾驶航空器的能源信息和状态信息；
根据所述能源信息和状态信息分别与子指令任务数据的续航信息和航路信息进行对比；
若所述能源信息和状态信息不能全部满足所述续航信息和航路信息，则格式化所述无人驾驶航空器的任务指令条，并将机队指令编码进行同步修正。
可选地，在本发明实施例所述的无人驾驶航空器的权限管控方法中，还包括：
实时读取所述机队的机队运行状态信息；
根据所述机队中无人驾驶航空器的任务指令条结合任务因子提取指令级数；
根据所述指令级数与所述机队运行状态信息对机队中所述无人驾驶航空器进行分级显示。
第二方面，本发明实施例提供了无人驾驶航空器的权限管控系统，该系统包括：存储器及处理器，所述存储器中包括无人驾驶航空器的权限管控方法的程序，所述无人驾驶航空器的权限管控方法的程序被所述处理器执行时实现以下步骤：
基于任务作业信息对目标区域内无人驾驶航空器进行编号得到机队指令编码；
提取所述任务作业信息的作业数据信息并输入预设任务指令数据库模型中获取无人驾驶航空器的指令任务数据；
根据所述指令任务数据结合机队指令编码得到无人驾驶航空器任务指令条并管理调度机队；
根据实时监测获取的机队所述无人驾驶航空器的任务状态信息，结合所述指令任务数据进行同步拟合获得权限匹配管理。
可选地，在本发明实施例所述的无人驾驶航空器的权限管控系统中，所述基于任务作业信息对目标区域内无人驾驶航空器进行编号得到机队指令编码，包括：
基于预设的任务作业信息提取任务因子得到目标区域内所需执行任务的无人驾驶航空器机队规模等级；
根据所述机队规模等级进行等级阈值匹配获得无人驾驶航空器型号和数量期望值；
根据所述无人驾驶航空器机队规模等级和期望值以及任务代码对机队中的无人驾驶航空器进行编号得到机队指令编码。
第三方面，本发明实施例还提供了一种计算机可读存储介质，所述计算机可读存储介质中包括无人驾驶航空器的权限管控方法程序，所述无人驾驶航空器的权限管控方法程序被处理器执行时，实现如上述任一项所述的无人驾驶航空器的权限管控方法的步骤。
由上可知，本发明实施例提供的一种无人驾驶航空器的权限管控方法、系统和存储介质通过基于任务作业信息对目标区域内无人驾驶航空器进行编号得到机队指令编码提取任务作业信息的作业数据信息并输入预设任务指令数据库模型中获取无人驾驶航空器的指令任务数据，并结合机队指令编码得到任务指令条并管理调度机队，根据实时监测获取的无人驾驶航空器的任务状态信息与指令任务数据进行同步拟合获得权限匹配管理；从而基于机队指令编码与获得的指令任务数据相结合获得任务指令条管理调度机队并通过任务状态信息与指令任务数据的同步拟合进行权限匹配管理的修正，提高对无人驾驶航空器任务指令权限的智能化精准管理调控。
本发明的其他特征和优点将在随后的说明书阐述，并且，部分地从说明书中变得显而易见，或者通过实施本发明实施例了解。本发明的目的和其他优点可通过在所写的说明书、权利要求书、以及附图中所特别指出的结构来实现和获得。
附图说明
为了更清楚地说明本发明实施例的技术方案，下面将对本发明实施例中所需要使用的附图作简单地介绍，应当理解，以下附图仅示出了本发明的某些实施例，因此不应被看作是对范围的限定，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他相关的附图。
图1为本发明实施例提供的无人驾驶航空器的权限管控方法的流程图；
图2为本发明实施例提供的无人驾驶航空器的权限管控方法的生成机队指令编码的流程图；
图3为本发明实施例提供的无人驾驶航空器的权限管控方法的获取无 人驾驶航空器的指令任务数据的流程图；
图4为本发明实施例提供的无人驾驶航空器的权限管控系统的一种结构示意图。
具体实施方式
下面将结合本发明实施例中附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。通常在此处附图中描述和示出的本发明实施例的组件可以以各种不同的配置来布置和设计。因此，以下对在附图中提供的本发明的实施例的详细描述并非旨在限制要求保护的本发明的范围，而是仅仅表示本发明的选定实施例。基于本发明的实施例，本领域技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例，都属于本发明保护的范围。
应注意到，相似的标号和字母在下面的附图中表示类似项，因此，一旦某一项在一个附图中被定义，则在随后的附图中不需要对其进行进一步定义和解释。同时，在本发明的描述中，术语“第一”、“第二”等仅用于区分描述，而不能理解为指示或暗示相对重要性。
请参照图1，图1是本发明一些实施例中的无人驾驶航空器的权限管控方法的流程图。该无人驾驶航空器的权限管控方法用于终端设备中，例如电脑、操控终端等。该无人驾驶航空器的权限管控方法，包括以下步骤：
S101、基于任务作业信息对目标区域内无人驾驶航空器进行编号得到机队指令编码；
S102、提取所述任务作业信息的作业数据信息并输入预设任务指令数据库模型中获取无人驾驶航空器的指令任务数据；
S103、根据所述指令任务数据结合机队指令编码得到无人驾驶航空器任务指令条并管理调度机队；
S104、根据实时监测获取的机队所述无人驾驶航空器的任务状态信息，结合所述指令任务数据进行同步拟合获得权限匹配管理。
需要说明的是，首先根据任务作业信息对目标区域内无人驾驶航空器进行编号得到机队指令编码，提取任务作业信息的作业数据信息并输入预设任务指令数据库模型中获取无人驾驶航空器的指令任务数据，再根据指令任务数据结合机队指令编码得到无人驾驶航空器任务指令条并管理调度机队，以及还包括根据实时监测获取的机队无人驾驶航空器的任务状态信息并与指令任务数据进行同步拟合获得权限匹配管理对无人驾驶航空器进行状态监管和权限调控。
请参照图2，图2是本发明一些实施例中的无人驾驶航空器的权限管控方法的生成机队指令编码的流程图。根据本发明实施例，所述基于任务作业信息对目标区域内无人驾驶航空器进行编号得到机队指令编码，具体为：
S201、基于预设的任务作业信息提取任务因子得到目标区域内所需执行任务的无人驾驶航空器机队规模等级；
S202、根据所述机队规模等级进行等级阈值匹配获得无人驾驶航空器型号和数量期望值；
S203、根据所述无人驾驶航空器机队规模等级和期望值以及任务代码对机队中的无人驾驶航空器进行编号得到机队指令编码。
需要说明的是，根据预设的任务作业信息提取任务因子，根据任务因子得到目标区域内所需执行任务的无人驾驶航空器机队规模等级，由于任务、工作量、覆盖区域、工作量级的不同使一定目标区域内需要执行任务的无人驾驶航空器机队规模也不同，因此，根据提取的任务因子可以获取对应的机队规模等级以满足任务需求，再根据机队规模等级进行等级阈值匹配以获取与等级阈值对应的无人驾驶航空器型号和数量期望值，如将等级阈值分为七个等级，阈值范围分别为[0，0.21]，(0.21，0.38]，(0.38，0.51]，(0.51，0.69]，(0.69，0.8]，(0.8，0.93]，(0.93，1.0]，不同等级对应不同的型号和数量，假设若机队A的机队规模等级对应等级阈值范围为(0.51，0.69]，对应第四级，而第四级的无人驾驶航空器的预设型号和数量分别B型2架、M型4架、S型6架、Y型3架，则机队A的型号和数量期望值为B2，M4，S6，Y3，再根据机队规模等级和期望值以及任务代码对机队中的无人 驾驶航空器进行编号得到机队指令编码，如机队A中的第2架B型无人驾驶航空器的机队指令编码为P4K6-IV-B2M4S6Y3-B2，同时机队的指令编码为P4K6-IV-B2M4S6Y3，其中P4K6为任务代码；
其中，机队规模等级计算公式为：
其中，En为机队规模等级，S0为目标区域规模值，θ为任务因子，为预设的区域特种系数，χ为任务特种系数。
请参照图3，图3是本发明一些实施例中的无人驾驶航空器的权限管控方法的获取无人驾驶航空器的指令任务数据的流程图。根据本发明实施例，所述提取所述任务作业信息的作业数据信息并输入预设任务指令数据库模型中获取无人驾驶航空器的指令任务数据，具体为：
S301、获取所述任务作业信息的作业数据信息，包括作业内容数据、作业体量数据、作业范围数据、作业级别数据；
S302、根据所述作业内容数据、作业体量数据、作业范围数据、作业级别数据输入至预设的任务指令数据库模型中获得指令任务数据；
S303、所述任务指令数据库模型根据历史无人驾驶航空器的作业内容数据、作业体量数据、作业范围数据、作业级别数据输入至初始任务指令数据库模型中训练获得。
需要说明的是，根据任务作业信息提取作业数据信息输入至预设的任务指令数据库模型中进行输出指令任务数据，任务指令数据库模型需要大量的历史数据进行训练，数据量越大则结果越准确，本方案中的预设任务指令数据库模型通过历史作业数据信息与指令任务数据作为输入进行训练，通过大量试验数据与真实数据比对以提高得到的结果的准确率，准确率阈值设置为95％。
根据本发明实施例，所述根据所述指令任务数据结合机队指令编码得到无人驾驶航空器任务指令条并管理调度机队，具体为：
根据所述指令任务数据提取子指令任务数据；
所述子指令任务数据对应机队中无人驾驶航空器的航路数据、目的地 数据、任务节点数据以及活动区域数据；
根据所述机队指令编码对所述无人驾驶航空器的子指令任务数据进行指令标注并生成任务指令条；
根据所述任务指令条对所述机队中各无人驾驶航空器进行调度控制。
需要说明的是，在获得机队的整体指令任务数据后，提取任务数据包中机队各无人驾驶航空器对应的子指令任务数据，再根据各个无人驾驶航空器的机队指令编码对子指令任务数据进行指令标注，生成任务指令条，则每个指令任务条对应包含了机队中每架无人驾驶航空器的指令任务的数据信息，根据生成的任务指令条对机队中相应的无人驾驶航空器进行指令发送、信息交互以及调度控制，实现对机队的各无人驾驶航空器的制定化调控。
根据本发明实施例，所述根据实时监测获取的机队所述无人驾驶航空器的任务状态信息，结合所述指令任务数据进行同步拟合获得权限匹配管理，具体为：
监测并提取机队所述无人驾驶航空器的实时任务状态信息；
所述任务状态信息包括所述无人驾驶航空器的动态航线数据、航向目标数据、实时任务数据以及动态飞行区域数据；
根据所述动态航线数据、航向目标数据、实时任务数据以及动态飞行区域数据与所述无人驾驶航空器的编号对应的子指令任务数据进行同步拟合度计算获得任务相似度；
根据所述任务相似度对所述机队中各无人驾驶航空器进行指令阈值对比；
将不符合阈值对比要求的无人驾驶航空器进行标注并对机队指令编码进行修改和权限修订。
需要说明的是，为监测机队中各无人驾驶航空器的任务执行状态，以匹配管控权限，使机队执行任务指令权限有效，需根据机队各航空器的执行指令状态匹配情况对航空器进行标注，并根据不满足任务状态的标注航空器对机队指令编码进行修改并同步完成权限修订，以满足对机队执行任 务状态的有效权限控制，确保机队完成执行任务，根据无人驾驶航空器的实时任务状态信息包括动态航线数据、航向目标数据、实时任务数据以及动态飞行区域数据与所述无人驾驶航空器的编号对应的子指令任务数据对应的航路数据、目的地数据、任务节点数据以及活动区域数据进行相似度拟合计算，所述相似度拟合计算结果即为同步拟合度计算结果，本方案中相似度拟合计算采用欧式距离相似度或余弦相似度进行计算获得任务相似度，再与机队中对应无人驾驶航空器进行预设指令阈值对比判断当前监测的无人驾驶航空器的任务执行状态是否满足预设指令要求，若阈值对比结果显示当前无人驾驶航空器的阈值对比结果不满足预设指令，则将该无人驾驶航空器进行标注并对机队指令编码进行修改和权限修订，或增设补丁其他备份无人驾驶航空器进行编码和指令调配。
根据本发明实施例，还包括：
对所述机队中各无人驾驶航空器进行模拟自检；
获取所述无人驾驶航空器的能源信息和状态信息；
根据所述能源信息和状态信息分别与子指令任务数据的续航信息和航路信息进行对比；
若所述能源信息和状态信息不能全部满足所述续航信息和航路信息，则格式化所述无人驾驶航空器的任务指令条，并将机队指令编码进行同步修正。
需要说明的是，在无人驾驶航空器执行任务指令之前，还需要对各无人驾驶航空器的能源信息和状态信息进行模拟自检以检定是否满足任务需求，例如，能源量不低于续航信息的75％同时续航状态量不少于航路信息的80％的无人驾驶航空器才准许执行任务，若不能同时满足上述两指标要求，则需要将该无人驾驶航空器进行指令格式化并同步修正机队指令编码，同时重新增补其他备份无人驾驶航空器以填空。
根据本发明实施例，还包括：
实时读取所述机队的机队运行状态信息；
根据所述机队中无人驾驶航空器的任务指令条结合任务因子提取指令 级数；
根据所述指令级数与所述机队运行状态信息对机队中所述无人驾驶航空器进行分级显示。
需要说明的是，机队中的各无人驾驶航空器执行任务指令多含多个任务指令条，根据不同指令条执行不同指令子任务，为明确各指令子任务的优先级和重要程度并可视化，根据任务因子结合任务指令条提取指令级数，根据指令级数与机队运行状态信息对指令级数对应机队中各无人驾驶航空器进行指令分级显示，以明确指令优先级顺序，便于掌握实时指令下的任务状态并可随时调控任务指令条以操控任务指令分配。
根据本发明实施例，还包括：
根据无人驾驶航空器任务和任务预案建立无人驾驶航空器任务调度数据库；
所述无人驾驶航空器任务调度数据库包括各类型无人驾驶航空器在不同历史任务中的任务等级和机群数量以及任务预案下的对应历史任务和应急响应等级；
根据所述无人驾驶航空器任务调度数据库中各历史任务的任务风险参数生成对应应急响应等级；
根据待派遣无人驾驶航空器类型、任务等级和机群数量在所述无人驾驶航空器任务调度数据库中进行相似度对比，获取与所述待派遣无人驾驶航空器类型、任务等级以及机群数量相似度符合预设值要求的无人驾驶航空器历史任务，作为所述待派遣无人驾驶航空器的目标任务；
根据获得的所述无人驾驶航空器历史任务对应的任务风险参数标记所述任务的应急响应等级；
根据所述应急响应等级对所述目标任务进行修正，制订所述待无人驾驶航空器派遣的修正任务，并派遣无人驾驶航空器进行任务。
需要说明的是，为预防派遣无人驾驶航空器的任务风险情况，避免恶劣工作环境和条件对无人驾驶航空器造成损坏，针对不同类型无人驾驶航空器在不同派遣任务、派出数量以及任务预案的工作条件下的任务和应急 措施统计建立无人驾驶航空器任务调度数据库，该数据库包含各类无人驾驶航空器的历史任务中的任务情况、派出数量以及对应任务和应急风险响应等级，可便于根据历史数据库对比获取待派遣无人驾驶航空器的最佳任务和任务风险情况，并针对任务的风险对任务指令进行修正，以降低任务风险和无人驾驶航空器损耗。
根据本发明实施例，还包括：
根据无人驾驶航空器状态信息设置无人驾驶航空器状态预设阈值；
所述无人驾驶航空器状态预设阈值根据所述类型无人驾驶航空器在执行任务的工作状态进行阈值范围划分；
所述无人驾驶航空器状态预设阈值划分为三个层级范围；
根据所述派遣的无人驾驶航空器的实时整机状态信息和运行轨迹数据信息获得所述无人驾驶航空器实时工作状态参数；
根据获得的无人驾驶航空器实时工作状态参数与所述无人驾驶航空器状态预设阈值进行阈值对比；
当所述无人驾驶航空器实时工作状态参数的阈值处于无人驾驶航空器状态第一预设阈值范围时，所述无人驾驶航空器继续执行任务；
当所述无人驾驶航空器实时工作状态参数的阈值处于无人驾驶航空器状态第二预设阈值范围时，中断所述无人驾驶航空器工作状态改为原地待命状态，等待检修无人驾驶航空器进行检修；
当所述无人驾驶航空器实时工作状态参数的阈值处于无人驾驶航空器状态第三预设阈值范围时，终止所述无人驾驶航空器工作任务并召回。
需要说明的是，根据各类型无人驾驶航空器在执行任务时的实时状态信息，将其工作状态制订划分为不同层级的阈值范围，根据制订的阈值范围对无人驾驶航空器实时工作状态信息参数进行阈值监控，当无人驾驶航空器实时工作状态信息参数处于不同预设阈值范围时，根据预设阈值范围对该无人驾驶航空器进行指令修正，根据本实施例无人驾驶航空器状态预设阈值范围划分为：[0,0.5),[0.5,0.8),[0.8,1.0]，当无人驾驶航空器实时工作状态参数的阈值处于第一预设阈值范围时继续执行任务，当处于第二预设 阈值范围时中断无人驾驶航空器工作状态改为原地待命状态等待检修无人驾驶航空器进行检修，当处于第三预设阈值范围时终止无人驾驶航空器工作任务并召回，其中，无人驾驶航空器实时工作状态参数根据派遣的无人驾驶航空器实时整机状态信息和运行轨迹数据信息获得。
如图4所示，本发明还公开了一种无人驾驶航空器的权限管控系统，包括存储器41和处理器42，所述存储器中包括无人驾驶航空器的权限管控方法程序，所述无人驾驶航空器的权限管控方法程序被所述处理器执行时实现如下步骤：
基于任务作业信息对目标区域内无人驾驶航空器进行编号得到机队指令编码；
提取所述任务作业信息的作业数据信息并输入预设任务指令数据库模型中获取无人驾驶航空器的指令任务数据；
根据所述指令任务数据结合机队指令编码得到无人驾驶航空器任务指令条并管理调度机队；
根据实时监测获取的机队所述无人驾驶航空器的任务状态信息，结合所述指令任务数据进行同步拟合获得权限匹配管理。
需要说明的是，首先根据任务作业信息对目标区域内无人驾驶航空器进行编号得到机队指令编码，提取任务作业信息的作业数据信息并输入预设任务指令数据库模型中获取无人驾驶航空器的指令任务数据，再根据指令任务数据结合机队指令编码得到无人驾驶航空器任务指令条并管理调度机队，以及还包括根据实时监测获取的机队无人驾驶航空器的任务状态信息并与指令任务数据进行同步拟合获得权限匹配管理对无人驾驶航空器进行状态监管和权限调控。
根据本发明实施例，所述基于任务作业信息对目标区域内无人驾驶航空器进行编号得到机队指令编码，具体为：
基于预设的任务作业信息提取任务因子得到目标区域内所需执行任务的无人驾驶航空器机队规模等级；
根据所述机队规模等级进行等级阈值匹配获得无人驾驶航空器型号和 数量期望值；
根据所述无人驾驶航空器机队规模等级和期望值以及任务代码对机队中的无人驾驶航空器进行编号得到机队指令编码。
需要说明的是，根据预设的任务作业信息提取任务因子，根据任务因子得到目标区域内所需执行任务的无人驾驶航空器机队规模等级，由于任务、工作量、覆盖区域、工作量级的不同使一定目标区域内需要执行任务的无人驾驶航空器机队规模也不同，因此，根据提取的任务因子可以获取对应的机队规模等级以满足任务需求，再根据机队规模等级进行等级阈值匹配以获取与等级阈值对应的无人驾驶航空器型号和数量期望值，如将等级阈值分为七个等级，阈值范围分别为[0，0.21]，(0.21，0.38]，(0.38，0.51]，(0.51，0.69]，(0.69，0.8]，(0.8，0.93]，(0.93，1.0]，不同等级对应不同的型号和数量，假设若机队A的机队规模等级对应等级阈值范围为(0.51，0.69]，对应第四级，而第四级的无人驾驶航空器的预设型号和数量分别B型2架、M型4架、S型6架、Y型3架，则机队A的型号和数量期望值为B2，M4，S6，Y3，再根据机队规模等级和期望值以及任务代码对机队中的无人驾驶航空器进行编号得到机队指令编码，如机队A中的第2架B型无人驾驶航空器的机队指令编码为P4K6-IV-B2M4S6Y3-B2，同时机队的指令编码为P4K6-IV-B2M4S6Y3，其中P4K6为任务代码；
其中，机队规模等级计算公式为：
其中，En为机队规模等级，S0为目标区域规模值，θ为任务因子，为预设的区域特种系数，χ为任务特种系数。
根据本发明实施例，所述提取所述任务作业信息的作业数据信息并输入预设任务指令数据库模型中获取无人驾驶航空器的指令任务数据，具体为：
获取所述任务作业信息的作业数据信息，包括作业内容数据、作业体量数据、作业范围数据、作业级别数据；
根据所述作业内容数据、作业体量数据、作业范围数据、作业级别数 据输入至预设的任务指令数据库模型中获得指令任务数据；
所述任务指令数据库模型根据历史无人驾驶航空器的作业内容数据、作业体量数据、作业范围数据、作业级别数据输入至初始任务指令数据库模型中训练获得。
需要说明的是，根据任务作业信息提取作业数据信息输入至预设的任务指令数据库模型中进行输出指令任务数据，任务指令数据库模型需要大量的历史数据进行训练，数据量越大则结果越准确，本方案中的预设任务指令数据库模型通过历史作业数据信息与指令任务数据作为输入进行训练，通过大量试验数据与真实数据比对以提高得到的结果的准确率，准确率阈值设置为95％。
根据本发明实施例，所述根据所述指令任务数据结合机队指令编码得到无人驾驶航空器任务指令条并管理调度机队，具体为：
根据所述指令任务数据提取子指令任务数据；
所述子指令任务数据对应机队中无人驾驶航空器的航路数据、目的地数据、任务节点数据以及活动区域数据；
根据所述机队指令编码对所述无人驾驶航空器的子指令任务数据进行指令标注并生成任务指令条；
根据所述任务指令条对所述机队中各无人驾驶航空器进行调度控制。
需要说明的是，在获得机队的整体指令任务数据后，提取任务数据包中机队各无人驾驶航空器对应的子指令任务数据，再根据各个无人驾驶航空器的机队指令编码对子指令任务数据进行指令标注，生成任务指令条，则每个指令任务条对应包含了机队中每架无人驾驶航空器的指令任务的数据信息，根据生成的任务指令条对机队中相应的无人驾驶航空器进行指令发送、信息交互以及调度控制，实现对机队的各无人驾驶航空器的制定化调控。
根据本发明实施例，所述根据实时监测获取的机队所述无人驾驶航空器的任务状态信息，结合所述指令任务数据进行同步拟合获得权限匹配管理，具体为：
监测并提取机队所述无人驾驶航空器的实时任务状态信息；
所述任务状态信息包括所述无人驾驶航空器的动态航线数据、航向目标数据、实时任务数据以及动态飞行区域数据；
根据所述动态航线数据、航向目标数据、实时任务数据以及动态飞行区域数据与所述无人驾驶航空器的编号对应的子指令任务数据进行同步拟合度计算获得任务相似度；
根据所述任务相似度对所述机队中各无人驾驶航空器进行指令阈值对比；
将不符合阈值对比要求的无人驾驶航空器进行标注并对机队指令编码进行修改和权限修订。
需要说明的是，为监测机队中各无人驾驶航空器的任务执行状态，以匹配管控权限，使机队执行任务指令权限有效，需根据机队各航空器的执行指令状态匹配情况对航空器进行标注，并根据不满足任务状态的标注航空器对机队指令编码进行修改并同步完成权限修订，以满足对机队执行任务状态的有效权限控制，确保机队完成执行任务，根据无人驾驶航空器的实时任务状态信息包括动态航线数据、航向目标数据、实时任务数据以及动态飞行区域数据与所述无人驾驶航空器的编号对应的子指令任务数据对应的航路数据、目的地数据、任务节点数据以及活动区域数据进行相似度拟合计算，所述相似度拟合计算结果即为同步拟合度计算结果，本方案中相似度拟合计算采用欧式距离相似度或余弦相似度进行计算获得任务相似度，再与机队中对应无人驾驶航空器进行预设指令阈值对比判断当前监测的无人驾驶航空器的任务执行状态是否满足预设指令要求，若阈值对比结果显示当前无人驾驶航空器的阈值对比结果不满足预设指令，则将该无人驾驶航空器进行标注并对机队指令编码进行修改和权限修订，或增设补丁其他备份无人驾驶航空器进行编码和指令调配。
根据本发明实施例，还包括：
对所述机队中各无人驾驶航空器进行模拟自检；
获取所述无人驾驶航空器的能源信息和状态信息；
根据所述能源信息和状态信息分别与子指令任务数据的续航信息和航路信息进行对比；
若所述能源信息和状态信息不能全部满足所述续航信息和航路信息，则格式化所述无人驾驶航空器的任务指令条，并将机队指令编码进行同步修正。
需要说明的是，在无人驾驶航空器执行任务指令之前，还需要对各无人驾驶航空器的能源信息和状态信息进行模拟自检以检定是否满足任务需求，例如，能源量不低于续航信息的75％同时续航状态量不少于航路信息的80％的无人驾驶航空器才准许执行任务，若不能同时满足上述两指标要求，则需要将该无人驾驶航空器进行指令格式化并同步修正机队指令编码，同时重新增补其他备份无人驾驶航空器以填空。
根据本发明实施例，还包括：
实时读取所述机队的机队运行状态信息；
根据所述机队中无人驾驶航空器的任务指令条结合任务因子提取指令级数；
根据所述指令级数与所述机队运行状态信息对机队中所述无人驾驶航空器进行分级显示。
需要说明的是，机队中的各无人驾驶航空器执行任务指令多含多个任务指令条，根据不同指令条执行不同指令子任务，为明确各指令子任务的优先级和重要程度并可视化，根据任务因子结合任务指令条提取指令级数，根据指令级数与机队运行状态信息对指令级数对应机队中各无人驾驶航空器进行指令分级显示，以明确指令优先级顺序，便于掌握实时指令下的任务状态并可随时调控任务指令条以操控任务指令分配。
根据本发明实施例，还包括：
根据无人驾驶航空器任务和任务预案建立无人驾驶航空器任务调度数据库；
所述无人驾驶航空器任务调度数据库包括各类型无人驾驶航空器在不同历史任务中的任务等级和机群数量以及任务预案下的对应历史任务和应 急响应等级；
根据所述无人驾驶航空器任务调度数据库中各历史任务的任务风险参数生成对应应急响应等级；
根据待派遣无人驾驶航空器类型、任务等级和机群数量在所述无人驾驶航空器任务调度数据库中进行相似度对比，获取与所述待派遣无人驾驶航空器类型、任务等级以及机群数量相似度符合预设值要求的无人驾驶航空器历史任务，作为所述待派遣无人驾驶航空器的目标任务；
根据获得的所述无人驾驶航空器历史任务对应的任务风险参数标记所述任务的应急响应等级；
根据所述应急响应等级对所述目标任务进行修正，制订所述待无人驾驶航空器派遣的修正任务，并派遣无人驾驶航空器进行任务。
需要说明的是，为预防派遣无人驾驶航空器的任务风险情况，避免恶劣工作环境和条件对无人驾驶航空器造成损坏，针对不同类型无人驾驶航空器在不同派遣任务、派出数量以及任务预案的工作条件下的任务和应急措施统计建立无人驾驶航空器任务调度数据库，该数据库包含各类无人驾驶航空器的历史任务中的任务情况、派出数量以及对应任务和应急风险响应等级，可便于根据历史数据库对比获取待派遣无人驾驶航空器的最佳任务和任务风险情况，并针对任务的风险对任务指令进行修正，以降低任务风险和无人驾驶航空器损耗。
根据本发明实施例，还包括：
根据无人驾驶航空器状态信息设置无人驾驶航空器状态预设阈值；
所述无人驾驶航空器状态预设阈值根据所述类型无人驾驶航空器在执行任务的工作状态进行阈值范围划分；
所述无人驾驶航空器状态预设阈值划分为三个层级范围；
根据所述派遣的无人驾驶航空器的实时整机状态信息和运行轨迹数据信息获得所述无人驾驶航空器实时工作状态参数；
根据获得的无人驾驶航空器实时工作状态参数与所述无人驾驶航空器状态预设阈值进行阈值对比；
当所述无人驾驶航空器实时工作状态参数的阈值处于无人驾驶航空器状态第一预设阈值范围时，所述无人驾驶航空器继续执行任务；
当所述无人驾驶航空器实时工作状态参数的阈值处于无人驾驶航空器状态第二预设阈值范围时，中断所述无人驾驶航空器工作状态改为原地待命状态，等待检修无人驾驶航空器进行检修；
当所述无人驾驶航空器实时工作状态参数的阈值处于无人驾驶航空器状态第三预设阈值范围时，终止所述无人驾驶航空器工作任务并召回。
需要说明的是，根据各类型无人驾驶航空器在执行任务时的实时状态信息，将其工作状态制订划分为不同层级的阈值范围，根据制订的阈值范围对无人驾驶航空器实时工作状态信息参数进行阈值监控，当无人驾驶航空器实时工作状态信息参数处于不同预设阈值范围时，根据预设阈值范围对该无人驾驶航空器进行指令修正，根据本实施例无人驾驶航空器状态预设阈值范围划分为：[0，0.5)，[0.5，0.8)，[0.8，1.0]，当无人驾驶航空器实时工作状态参数的阈值处于第一预设阈值范围时继续执行任务，当处于第二预设阈值范围时中断无人驾驶航空器工作状态改为原地待命状态等待检修无人驾驶航空器进行检修，当处于第三预设阈值范围时终止无人驾驶航空器工作任务并召回，其中，无人驾驶航空器实时工作状态参数根据派遣的无人驾驶航空器实时整机状态信息和运行轨迹数据信息获得。
本发明第三方面提供了一种可读存储介质，所述可读存储介质中包括无人驾驶航空器的权限管控方法程序，所述无人驾驶航空器的权限管控方法程序被处理器执行时，实现如上述任一项所述的无人驾驶航空器的权限管控方法的步骤。
本发明公开的一种无人驾驶航空器的权限管控方法、系统和存储介质，通过基于任务作业信息对目标区域内无人驾驶航空器进行编号得到机队指令编码提取任务作业信息的作业数据信息并输入预设任务指令数据库模型中获取无人驾驶航空器的指令任务数据，并结合机队指令编码得到任务指令条并管理调度机队，根据实时监测获取的无人驾驶航空器的任务状态信息与指令任务数据进行同步拟合获得权限匹配管理；从而基于机队指令编 码与获得的指令任务数据相结合获得任务指令条管理调度机队并通过任务状态信息与指令任务数据的同步拟合进行权限匹配管理的修正，提高对无人驾驶航空器任务指令权限的智能化精准管理调控。
在本发明所提供的几个实施例中，应该理解到，所揭露的设备和方法，可以通过其它的方式实现。以上所描述的设备实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，如：多个单元或组件可以结合，或可以集成到另一个系统，或一些特征可以忽略，或不执行。另外，所显示或讨论的各组成部分相互之间的耦合、或直接耦合、或通信连接可以是通过一些接口，设备或单元的间接耦合或通信连接，可以是电性的、机械的或其它形式的。
上述作为分离部件说明的单元可以是、或也可以不是物理上分开的，作为单元显示的部件可以是、或也可以不是物理单元；既可以位于一个地方，也可以分布到多个网络单元上；可以根据实际的需要选择其中的部分或全部单元来实现本实施例方案的目的。
另外，在本发明各实施例中的各功能单元可以全部集成在一个处理单元中，也可以是各单元分别单独作为一个单元，也可以两个或两个以上单元集成在一个单元中；上述集成的单元既可以采用硬件的形式实现，也可以采用硬件加软件功能单元的形式实现。
本领域普通技术人员可以理解：实现上述方法实施例的全部或部分步骤可以通过程序指令相关的硬件来完成，前述的程序可以存储于可读取存储介质中，该程序在执行时，执行包括上述方法实施例的步骤；而前述的存储介质包括：移动存储设备、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
或者，本发明上述集成的单元如果以软件功能模块的形式实现并作为独立的产品销售或使用时，也可以存储在一个可读取存储介质中。基于这样的理解，本发明实施例的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该软件产品存储在一个存储介质中， 包括若干指令用以使得一台计算机设备(可以是个人计算机、服务器、或者网络设备等)执行本发明各个实施例所述方法的全部或部分。而前述的存储介质包括：移动存储设备、ROM、RAM、磁碟或者光盘等各种可以存储程序代码的介质。

Claims (10)

  1. 一种无人驾驶航空器的权限管控方法，其特征在于，包括以下步骤：
    基于任务作业信息对目标区域内无人驾驶航空器进行编号得到机队指令编码；
    提取所述任务作业信息的作业数据信息并输入预设任务指令数据库模型中获取无人驾驶航空器的指令任务数据；
    根据所述指令任务数据结合机队指令编码得到无人驾驶航空器任务指令条并管理调度机队；
    根据实时监测获取的机队所述无人驾驶航空器的任务状态信息，结合所述指令任务数据进行同步拟合获得权限匹配管理。
  2. 根据权利要求1所述的无人驾驶航空器的权限管控方法，其特征在于，所述基于任务作业信息对目标区域内无人驾驶航空器进行编号得到机队指令编码，包括：
    基于预设的任务作业信息提取任务因子得到目标区域内所需执行任务的无人驾驶航空器机队规模等级；
    根据所述机队规模等级进行等级阈值匹配获得无人驾驶航空器型号和数量期望值；
    根据所述无人驾驶航空器机队规模等级和期望值以及任务代码对机队中的无人驾驶航空器进行编号得到机队指令编码。
  3. 根据权利要求2所述的无人驾驶航空器的权限管控方法，其特征在于，所述提取所述任务作业信息的作业数据信息并输入预设任务指令数据库模型中获取无人驾驶航空器的指令任务数据，包括：
    获取所述任务作业信息的作业数据信息，包括作业内容数据、作业体量数据、作业范围数据、作业级别数据；
    根据所述作业内容数据、作业体量数据、作业范围数据、作业级别数据输入至预设的任务指令数据库模型中获得指令任务数据；
    所述任务指令数据库模型根据历史无人驾驶航空器的作业内容数据、 作业体量数据、作业范围数据、作业级别数据输入至初始任务指令数据库模型中训练获得。
  4. 根据权利要求3所述的无人驾驶航空器的权限管控方法，其特征在于，所述根据所述指令任务数据结合机队指令编码得到无人驾驶航空器任务指令条并管理调度机队，包括：
    根据所述指令任务数据提取子指令任务数据；
    所述子指令任务数据对应机队中无人驾驶航空器的航路数据、目的地数据、任务节点数据以及活动区域数据；
    根据所述机队指令编码对所述无人驾驶航空器的子指令任务数据进行指令标注并生成任务指令条；
    根据所述任务指令条对所述机队中各无人驾驶航空器进行调度控制。
  5. 根据权利要求4所述的无人驾驶航空器的权限管控方法，其特征在于，所述根据实时监测获取的机队所述无人驾驶航空器的任务状态信息，结合所述指令任务数据进行同步拟合获得权限匹配管理，包括：
    监测并提取机队所述无人驾驶航空器的实时任务状态信息；
    所述任务状态信息包括所述无人驾驶航空器的动态航线数据、航向目标数据、实时任务数据以及动态飞行区域数据；
    根据所述动态航线数据、航向目标数据、实时任务数据以及动态飞行区域数据与所述无人驾驶航空器的编号对应的子指令任务数据进行同步拟合度计算获得任务相似度；
    根据所述任务相似度对所述机队中各无人驾驶航空器进行指令阈值对比；
    将不符合阈值对比要求的无人驾驶航空器进行标注并对机队指令编码进行修改和权限修订。
  6. 根据权利要求1所述的无人驾驶航空器的权限管控方法，其特征在于，还包括：
    对所述机队中各无人驾驶航空器进行模拟自检；
    获取所述无人驾驶航空器的能源信息和状态信息；
    根据所述能源信息和状态信息分别与子指令任务数据的续航信息和航路信息进行对比；
    若所述能源信息和状态信息不能全部满足所述续航信息和航路信息，则格式化所述无人驾驶航空器的任务指令条，并将机队指令编码进行同步修正。
  7. 根据权利要求1所述的无人驾驶航空器的权限管控方法，其特征在于，还包括：
    实时读取所述机队的机队运行状态信息；
    根据所述机队中无人驾驶航空器的任务指令条结合任务因子提取指令级数；
    根据所述指令级数与所述机队运行状态信息对机队中所述无人驾驶航空器进行分级显示。
  8. 一种无人驾驶航空器的权限管控系统，其特征在于，该系统包括：存储器及处理器，所述存储器中包括无人驾驶航空器的权限管控方法的程序，所述无人驾驶航空器的权限管控方法的程序被所述处理器执行时实现以下步骤：
    基于任务作业信息对目标区域内无人驾驶航空器进行编号得到机队指令编码；
    提取所述任务作业信息的作业数据信息并输入预设任务指令数据库模型中获取无人驾驶航空器的指令任务数据；
    根据所述指令任务数据结合机队指令编码得到无人驾驶航空器任务指令条并管理调度机队；
    根据实时监测获取的机队所述无人驾驶航空器的任务状态信息，结合所述指令任务数据进行同步拟合获得权限匹配管理。
  9. 根据权利要求8所述的无人驾驶航空器的权限管控系统，其特征在于，所述基于任务作业信息对目标区域内无人驾驶航空器进行编号得到机队指令编码，包括：
    基于预设的任务作业信息提取任务因子得到目标区域内所需执行任务 的无人驾驶航空器机队规模等级；
    根据所述机队规模等级进行等级阈值匹配获得无人驾驶航空器型号和数量期望值；
    根据所述无人驾驶航空器机队规模等级和期望值以及任务代码对机队中的无人驾驶航空器进行编号得到机队指令编码。
  10. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质中包括无人驾驶航空器的权限管控方法程序，所述无人驾驶航空器的权限管控方法程序被处理器执行时，实现如权利要求1至7中任一项所述的无人驾驶航空器的权限管控方法的步骤。
PCT/CN2023/134145 2022-11-28 2023-11-24 一种无人驾驶航空器的权限管控方法、系统和存储介质 WO2024114543A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202211502077.X 2022-11-28
CN202211502077.XA CN115829260A (zh) 2022-11-28 2022-11-28 一种无人驾驶航空器的权限管控方法、系统和存储介质

Publications (1)

Publication Number Publication Date
WO2024114543A1 true WO2024114543A1 (zh) 2024-06-06

Family

ID=85532216

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2023/134145 WO2024114543A1 (zh) 2022-11-28 2023-11-24 一种无人驾驶航空器的权限管控方法、系统和存储介质

Country Status (2)

Country Link
CN (1) CN115829260A (zh)
WO (1) WO2024114543A1 (zh)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115829260A (zh) * 2022-11-28 2023-03-21 亿航智能设备（广州）有限公司 一种无人驾驶航空器的权限管控方法、系统和存储介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180004202A1 (en) * 2016-07-01 2018-01-04 Innovation Matrix, Inc. Robot fleet dispatch request system
CN112433539A (zh) * 2020-11-27 2021-03-02 佛山市墨纳森智能科技有限公司 一种处理高速交通事故的无人机调度方法、系统及可读存储介质
CN114298552A (zh) * 2021-12-28 2022-04-08 广东电网有限责任公司 无人机多机协同作业管理方法、系统和可读存储介质
CN115829260A (zh) * 2022-11-28 2023-03-21 亿航智能设备（广州）有限公司 一种无人驾驶航空器的权限管控方法、系统和存储介质

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180004202A1 (en) * 2016-07-01 2018-01-04 Innovation Matrix, Inc. Robot fleet dispatch request system
CN112433539A (zh) * 2020-11-27 2021-03-02 佛山市墨纳森智能科技有限公司 一种处理高速交通事故的无人机调度方法、系统及可读存储介质
CN114298552A (zh) * 2021-12-28 2022-04-08 广东电网有限责任公司 无人机多机协同作业管理方法、系统和可读存储介质
CN115829260A (zh) * 2022-11-28 2023-03-21 亿航智能设备（广州）有限公司 一种无人驾驶航空器的权限管控方法、系统和存储介质

Also Published As

Publication number Publication date
CN115829260A (zh) 2023-03-21

Similar Documents

Publication Publication Date Title
CN111210184B (zh) 一种数字孪生车间物料准时配送方法及系统
WO2024114543A1 (zh) 一种无人驾驶航空器的权限管控方法、系统和存储介质
US8185298B2 (en) Hybrid heuristic national airspace flight path optimization
CN108736465A (zh) 一种未来态电网模型构建方法和装置
CN109118097B (zh) 一种可靠性维修性保障性评估方法和装置
CN110717685A (zh) 建筑物料管理系统及方法
CN106127365A (zh) 定量遥感产品在线交互式自主生产方法
van der Weide et al. Robust long-term aircraft heavy maintenance check scheduling optimization under uncertainty
CN109086532A (zh) 一种基于oopn网模型的立体仓库半实物仿真建模方法
CN114818303A (zh) 低碳园区的仿真规划方法、系统、电子设备及存储介质
CN103544345A (zh) 操作票智能防误仿真系统
CN109801364A (zh) 一种三维数字建模方法和数字化车间管理系统
CN109656589A (zh) 一种智能变电站二次设备软件管控方法及系统
CN109979244B (zh) 异质飞行器空域拥堵的预测方法和装置
CN116663098A (zh) 基于bim的施工场景图形化管理方法及装置
CN113625770B (zh) 基于飞行无人机对光伏电站进行巡检的自主导航规划方法及装置
CN116187675A (zh) 任务分配方法、装置、设备及存储介质
US20220342399A1 (en) Distributed production planning and instruction apparatus and distributed production planning and instruction system
CN114925908A (zh) 装配式建筑施工进度的信息化控制方法
CN115674198A (zh) 一种对机器人进行部署的方法、装置、设备及存储介质
CN115271821A (zh) 网点分布处理方法、装置、计算机设备及存储介质
CN114971922A (zh) 车辆勘察的定损方法、装置、设备及存储介质
Wu et al. Integration of BIM and computer simulations in modular construction, A case study
CN111627255B (zh) 信息处理方法、装置和系统
Zikos et al. Human-Resources optimization & re-adaptation modelling in enterprises

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23896694

Country of ref document: EP

Kind code of ref document: A1