CN115829260A - Authority management and control method and system of unmanned aircraft and storage medium - Google Patents
Authority management and control method and system of unmanned aircraft and storage medium Download PDFInfo
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Abstract
The embodiment of the invention provides a method, a system and a storage medium for managing and controlling the authority of an unmanned aircraft. The method comprises the following steps: numbering unmanned aircraft in a target area based on task operation information to obtain a fleet instruction code, extracting operation data information of the task operation information, inputting the operation data information into a preset task instruction database model to obtain instruction task data of the unmanned aircraft, obtaining a task instruction strip by combining the fleet instruction code, managing and dispatching the fleet, and performing synchronous fitting according to task state information of the unmanned aircraft obtained through real-time monitoring and the instruction task data to obtain authority matching management; therefore, the crew is managed and dispatched based on the crew instruction codes and the obtained instruction task data, authority matching management is corrected through synchronous fitting of task state information and the instruction task data, and intelligent accurate management and control of task instruction authority of the unmanned aircraft are improved.
Description
Technical Field
The invention relates to the technical field of unmanned aircraft management, in particular to a method and a system for managing and controlling the authority of an unmanned aircraft and a storage medium.
Background
The unmanned aerial vehicle is widely applied at present, can improve the transport capacity of remote areas, improve the agricultural production efficiency and solve the problems of urban logistics and urban planning and construction management, has universal significance, and is more and more remarkable in terms of safety management of the unmanned aerial vehicle along with the further increase and expansion of the types and the purposes of the unmanned aerial vehicle.
At present, in the application fields of cooperative operation of unmanned aircraft fleet and fleet, such as exploration, agricultural seeding and the like, due to the fact that scientific short boards of fleet system management and scheduling dimensionality exist, the defects that control errors or confusion exist in traditional terminal control, program control is prone to occurring in disorder of authority and poor flexibility and mobility exist easily exist, and therefore the intelligent technology for multi-machine cooperative authority control of the unmanned aircraft is to be solved urgently.
In view of the above problems, an effective technical solution is urgently needed.
Disclosure of Invention
The embodiment of the invention aims to provide a permission management and control method, a permission management and control system and a storage medium of an unmanned aerial vehicle, which can obtain a task instruction strip for managing and scheduling a fleet according to the combination of fleet instruction codes and obtained instruction task data, correct the management progression of matched permissions and improve the intelligent accurate management and control of task instruction permissions of the unmanned aerial vehicle.
The embodiment of the invention also provides a permission management and control method of the unmanned aircraft, which comprises the following steps:
numbering the unmanned aircraft in the target area based on the task operation information to obtain a fleet instruction code;
extracting operation data information of the task operation information and inputting the operation data information into a preset task instruction database model to obtain instruction task data of the unmanned aircraft;
combining the command task data with the fleet command codes to obtain a task command strip of the unmanned aircraft and managing and dispatching the fleet;
and according to the task state information of the unmanned aircraft of the fleet obtained through real-time monitoring, synchronous fitting is carried out by combining the instruction task data to obtain authority matching management.
Optionally, in the method for authority control of an unmanned aerial vehicle according to the embodiment of the present invention, the numbering the unmanned aerial vehicle in the target area based on the task operation information to obtain a fleet instruction code includes:
extracting task factors based on preset task operation information to obtain the unmanned aircraft fleet scale grade of the tasks required to be executed in the target area;
carrying out grade threshold matching according to the fleet scale grade to obtain the model and quantity expected values of the unmanned aircraft;
and numbering the unmanned aircraft in the fleet according to the scale grade and the expected value of the unmanned aircraft fleet and the task code to obtain a fleet instruction code.
Optionally, in the method for authority control of an unmanned aerial vehicle according to the embodiment of the present invention, the extracting the operation data information of the task operation information and inputting the operation data information into a preset task instruction database model to obtain instruction task data of the unmanned aerial vehicle includes:
acquiring job data information of the task job information, wherein the job data information comprises job content data, job size data, job range data and job level data;
inputting the operation content data, the operation volume data, the operation range data and the operation level data into a preset task instruction database model to obtain instruction task data;
and the task instruction database model is input into the initial task instruction database model for training to obtain according to the operation content data, the operation volume data, the operation range data and the operation level data of the historical unmanned aircraft.
Optionally, in the method for authority management and control of an unmanned aerial vehicle according to the embodiment of the present invention, the obtaining a task instruction strip of the unmanned aerial vehicle and managing a fleet of aircraft according to the instruction task data and combining with a fleet instruction code includes:
extracting sub-instruction task data according to the instruction task data;
the sub-instruction task data correspond to route data, destination data, task node data and activity area data of unmanned aircraft in the fleet;
performing instruction marking on sub-instruction task data of the unmanned aerial vehicle according to the fleet instruction codes and generating a task instruction strip;
and carrying out scheduling control on each unmanned aircraft in the fleet according to the task instruction strip.
Optionally, in the method for managing and controlling authority of an unmanned aerial vehicle according to the embodiment of the present invention, the performing synchronous fitting according to the task state information of the unmanned aerial vehicle of the fleet obtained through real-time monitoring and in combination with the instruction task data to obtain authority matching management includes:
monitoring and extracting real-time task state information of the unmanned aircraft of the fleet;
the task state information comprises dynamic course data, course target data, real-time task data and dynamic flight area data of the unmanned aerial vehicle;
performing synchronous fitting degree calculation according to the dynamic route data, the course target data, the real-time task data and the sub-instruction task data corresponding to the number of the unmanned aircraft and the dynamic flight area data to obtain task similarity;
comparing instruction thresholds of all unmanned aircraft in the fleet according to the task similarity;
and marking the unmanned aircraft which does not meet the threshold comparison requirement, and modifying the command codes of the fleet.
Optionally, in the method for authority control of an unmanned aerial vehicle according to the embodiment of the present invention, the method further includes:
carrying out simulation self-inspection on each unmanned aircraft in the fleet;
acquiring energy information and state information of the unmanned aircraft;
respectively comparing the energy information and the state information with the endurance information and the airway information of the sub-instruction task data;
and if the energy information and the state information can not meet the cruising information and the route information, formatting a task instruction strip of the unmanned aerial vehicle, and synchronously correcting the command codes of the fleet.
Optionally, in the method for authority control of an unmanned aerial vehicle according to the embodiment of the present invention, the method further includes:
reading the fleet running state information of the fleet in real time;
extracting instruction series according to a task instruction strip of the unmanned aircraft in the fleet in combination with a task factor;
and displaying the unmanned aircraft in the fleet in a grading manner according to the instruction series and the fleet running state information.
In a second aspect, an embodiment of the present invention provides a system for authority management and control of an unmanned aircraft, where the system includes: a memory including a program of a method for authority management of an unmanned aircraft, and a processor, wherein the program of the method for authority management of an unmanned aircraft when executed by the processor implements the steps of:
numbering the unmanned aircraft in the target area based on the task operation information to obtain a fleet command code;
extracting operation data information of the task operation information and inputting the operation data information into a preset task instruction database model to obtain instruction task data of the unmanned aircraft;
combining the command task data with the fleet command codes to obtain a task command strip of the unmanned aircraft and managing and dispatching the fleet;
and according to the task state information of the unmanned aircraft of the fleet obtained through real-time monitoring, synchronous fitting is carried out by combining the instruction task data to obtain authority matching management.
Optionally, in the authority management and control system for an unmanned aerial vehicle according to the embodiment of the present invention, the numbering the unmanned aerial vehicle in the target area based on the task operation information to obtain a fleet instruction code includes:
extracting task factors based on preset task operation information to obtain the scale grade of the unmanned aircraft fleet which needs to execute the task in the target area;
carrying out grade threshold matching according to the fleet scale grade to obtain the model and quantity expected values of the unmanned aircraft;
and numbering the unmanned aircraft in the fleet according to the unmanned aircraft fleet scale grade, the expected value and the task code to obtain a fleet instruction code.
In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a program of a method for authority management of an unmanned aircraft, and when the program of the method for authority management of an unmanned aircraft is executed by a processor, the method for authority management of an unmanned aircraft implements the steps of the method for authority management of an unmanned aircraft according to any one of the above items.
As can be seen from the above, the method, the system and the storage medium for managing and controlling the authority of the unmanned aerial vehicle provided by the embodiment of the invention obtain the crew instruction code by numbering the unmanned aerial vehicle in the target area based on the task operation information, extract the operation data information of the task operation information, input the operation data information into the preset task instruction database model to obtain the instruction task data of the unmanned aerial vehicle, obtain the task instruction strip by combining the crew instruction code and manage the scheduling crew, and perform synchronous fitting according to the task state information of the unmanned aerial vehicle obtained by real-time monitoring and the instruction task data to obtain the authority matching management; therefore, the crew is managed and dispatched based on the crew instruction codes and the obtained instruction task data, authority matching management is corrected through synchronous fitting of task state information and the instruction task data, and intelligent accurate management and control of task instruction authority of the unmanned aircraft are improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments of the present invention will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of a method for authority management and control of an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 2 is a flowchart of generating a fleet instruction code according to a method for authority control of an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 3 is a flowchart of acquiring instruction task data of an unmanned aerial vehicle according to the method for managing and controlling authority of an unmanned aerial vehicle provided by the embodiment of the present invention;
fig. 4 is a schematic structural diagram of a privilege management and control system of an unmanned aerial vehicle according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that like reference numerals and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, fig. 1 is a flowchart of a method for authority management of an unmanned aircraft according to some embodiments of the present disclosure. The method for managing and controlling the authority of the unmanned aircraft is used for terminal equipment, such as a computer, a control terminal and the like. The method for managing and controlling the authority of the unmanned aircraft comprises the following steps:
s101, numbering the unmanned aircraft in the target area based on task operation information to obtain a fleet instruction code;
s102, extracting operation data information of the task operation information and inputting the operation data information into a preset task instruction database model to obtain instruction task data of the unmanned aircraft;
s103, combining the command task data with a fleet command code to obtain a task command strip of the unmanned aerial vehicle and managing and dispatching a fleet;
and S104, performing synchronous fitting according to the task state information of the unmanned aircraft of the fleet obtained through real-time monitoring and combining the instruction task data to obtain authority matching management.
The method includes the steps of firstly numbering unmanned aircraft in a target area according to task operation information to obtain a fleet instruction code, extracting operation data information of the task operation information, inputting the operation data information into a preset task instruction database model to obtain instruction task data of the unmanned aircraft, then combining the fleet instruction code with the instruction task data to obtain a task instruction strip of the unmanned aircraft and managing and dispatching the fleet, and further performing state supervision and authority regulation on the unmanned aircraft according to task state information of the unmanned aircraft of the fleet obtained through real-time monitoring and synchronous fitting with the instruction task data to obtain authority matching management.
Referring to fig. 2, fig. 2 is a flowchart of a fleet instruction generation method for the jurisdiction control method of an unmanned aerial vehicle according to some embodiments of the invention. According to the embodiment of the invention, the numbering of the unmanned aerial vehicle in the target area based on the task operation information to obtain the fleet command code specifically comprises the following steps:
s201, extracting task factors based on preset task operation information to obtain the scale grade of the unmanned aircraft fleet which needs to execute tasks in a target area;
s202, performing grade threshold matching according to the size grade of the fleet to obtain expected values of the model and the quantity of the unmanned aircraft;
s203, numbering the unmanned aircraft in the fleet according to the unmanned aircraft fleet scale grade, the expected value and the task code to obtain a fleet instruction code.
It should be noted that, a task factor is extracted according to preset task operation information, and the scale grade of the unmanned aircraft fleet which needs to execute the task in a target area is obtained according to the task factor, because the scales of the unmanned aircraft fleet which needs to execute the task in a certain target area are different due to the difference of the task, the workload, the coverage area and the workload level, the corresponding fleet scale grade can be obtained according to the extracted task factor to meet the task requirement, and then grade threshold value matching is performed according to the fleet scale grade to obtain the unmanned aircraft model and the number expected value corresponding to the grade threshold value, for example, the grade threshold value is divided into seven grades, and the threshold value ranges are respectively [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], the different levels correspond to different models and numbers, and if the fleet scale level of the fleet a corresponds to the level threshold range of (0.51, 0.69], corresponding to the fourth level and the preset models and numbers of the unmanned aircrafts of the fourth level are respectively B-type 2, M-type 4, S-type 6 and Y-type 3, the desired values of the models and numbers of the fleet a are B2, M4, S6 and Y3, and the unmanned aircrafts in the fleet are numbered according to the fleet scale level and the desired values and the mission code to obtain a fleet command code, for example, the fleet command code of the fleet of the unmanned aircrafts of the second fleet in the fleet a is P4K6-IV-B2M4S6Y3, and the command code of the fleet is P4K6-IV-B2M4S6Y3, wherein P4K6 is the mission code;
wherein, the calculation formula of the airplane fleet scale grade is as follows:
wherein E is n For fleet size classes, S 0 Is the target area scale value, theta is the task factor,
is a preset region special coefficient, and x is a task special coefficient.
Referring to fig. 3, fig. 3 is a flowchart of a method for authority management of an unmanned aerial vehicle according to some embodiments of the present invention for obtaining instruction task data of the unmanned aerial vehicle. According to the embodiment of the invention, the extracting of the operation data information of the task operation information and the inputting of the operation data information into a preset task instruction database model to obtain the instruction task data of the unmanned aerial vehicle specifically comprise:
s301, acquiring job data information of the task job information, wherein the job data information comprises job content data, job size data, job range data and job level data;
s302, inputting the job content data, the job size data, the job range data and the job level data into a preset task instruction database model to obtain instruction task data;
and S303, inputting the task instruction database model into an initial task instruction database model for training according to the operation content data, the operation volume data, the operation range data and the operation level data of the historical unmanned aircraft.
It should be noted that, the operation data information is extracted according to the task operation information and input into the preset task instruction database model to output instruction task data, the task instruction database model requires a large amount of historical data to train, the larger the data volume is, the more accurate the result is, the preset task instruction database model in the scheme trains by taking the historical operation data information and the instruction task data as input, the accuracy of the obtained result is improved by comparing a large amount of test data with real data, and the accuracy threshold is set to 95%.
According to the embodiment of the invention, the obtaining of the unmanned aircraft task instruction strip and the management and dispatching of the fleet according to the instruction task data and the fleet instruction codes are specifically as follows:
extracting sub-instruction task data according to the instruction task data;
the sub-instruction task data correspond to route data, destination data, task node data and activity area data of unmanned aircraft in the fleet;
performing instruction marking on sub-instruction task data of the unmanned aerial vehicle according to the fleet instruction codes and generating a task instruction strip;
and carrying out scheduling control on each unmanned aircraft in the fleet according to the task instruction strip.
It should be noted that after the whole instruction task data of the fleet is obtained, the sub-instruction task data corresponding to each unmanned aerial vehicle of the fleet in the task data packet is extracted, then instruction labeling is performed on the sub-instruction task data according to the fleet instruction codes of each unmanned aerial vehicle, and task instruction bars are generated, so that each instruction task bar correspondingly contains data information of the instruction task of each unmanned aerial vehicle in the fleet, and instruction sending, information interaction and scheduling control are performed on the corresponding unmanned aerial vehicle in the fleet according to the generated task instruction bars, so that customized regulation and control of each unmanned aerial vehicle of the fleet is realized.
According to the embodiment of the invention, the task state information of the unmanned aircraft of the fleet obtained through real-time monitoring is combined with the instruction task data to perform synchronous fitting to obtain authority matching management, and the authority matching management specifically comprises the following steps:
monitoring and extracting real-time task state information of the unmanned aircraft of the fleet;
the task state information comprises dynamic course data, course target data, real-time task data and dynamic flight area data of the unmanned aerial vehicle;
performing synchronous fitting degree calculation according to the dynamic route data, the course target data, the real-time task data and the sub-instruction task data corresponding to the number of the unmanned aircraft and the dynamic flight area data to obtain task similarity;
comparing instruction thresholds of all unmanned aircraft in the fleet according to the task similarity;
and marking the unmanned aircraft which does not meet the threshold comparison requirement, and modifying the command codes of the fleet.
The method includes the steps that in order to monitor task execution states of unmanned aircrafts in a fleet and match control authorities to enable the fleet to effectively execute task instruction authorities, the aircrafts need to be labeled according to the execution instruction state matching conditions of the aircrafts in the fleet, fleet instruction codes are modified according to labeled aircrafts which do not meet the task states and synchronously complete authority revision to meet effective authority control of the fleet to execute the task states, the aircrafts are guaranteed to complete execution tasks, similarity fitting calculation is conducted according to real-time task state information of the unmanned aircrafts, including dynamic course data, course target data, real-time task data and dynamic flight area data, and path data, destination data, task node data and active area data corresponding to sub-instruction task data corresponding to the numbers of the unmanned aircrafts, the similarity calculation result is a synchronous fitting degree calculation result, the similarity fitting calculation adopts Euclidean distance similarity or cosine similarity to calculate task similarity, and judges whether the current monitored unmanned aircraft execution state of the unmanned aircrafts meets preset instruction comparison requirements of a preset aircraft marking threshold or not, and the unmanned aircraft marking instructions are added to corresponding unmanned aircrafts in comparison results, and whether the unmanned aircraft editing unmanned aircrafts and other unmanned aircraft codes meet the preset instructions or not.
According to the embodiment of the invention, the method further comprises the following steps:
carrying out simulation self-inspection on each unmanned aircraft in the fleet;
acquiring energy information and state information of the unmanned aircraft;
respectively comparing the energy information and the state information with the endurance information and the airway information of the sub-instruction task data;
and if the energy information and the state information can not completely meet the endurance information and the route information, formatting a task instruction strip of the unmanned aerial vehicle, and synchronously correcting the command codes of the fleet.
Before the unmanned aircraft executes the mission command, the energy information and the state information of each unmanned aircraft are required to be subjected to simulated self-checking to verify whether the mission requirement is met, for example, the unmanned aircraft with the energy amount not less than 75% of the cruising information and the cruising state amount not less than 80% of the route information is permitted to execute the mission, and if the two index requirements cannot be met simultaneously, the unmanned aircraft is required to be subjected to command formatting and synchronous correction of fleet command codes, and other backup unmanned aircraft are supplemented again to fill the air.
According to the embodiment of the invention, the method further comprises the following steps:
reading the fleet running state information of the fleet in real time;
extracting instruction series according to a task instruction strip of the unmanned aircraft in the fleet in combination with a task factor;
and displaying the unmanned aircraft in the fleet in a grading manner according to the instruction series and the fleet running state information.
It should be noted that each unmanned aerial vehicle in the fleet executes task instructions and mainly comprises a plurality of task instruction bars, different instruction subtasks are executed according to different instruction bars, in order to determine the priority and the importance degree of each instruction subtask and visualize the priority and the importance degree of each instruction subtask, the order number is extracted according to task factors and the task instruction bars, and each unmanned aerial vehicle in the fleet corresponding to the order number is subjected to order grading display according to the order number and the fleet running state information, so that the order priority order is determined, the task state under the real-time instruction can be conveniently mastered, and the task instruction bars can be regulated and controlled at any time to control task instruction allocation.
According to the embodiment of the invention, the method further comprises the following steps:
establishing a task scheduling database of the unmanned aircraft according to the tasks and the task plan of the unmanned aircraft;
the task scheduling database of the unmanned aircraft comprises task grades and cluster numbers of various types of unmanned aircraft in different historical tasks and corresponding historical tasks and emergency response grades under a task plan;
generating corresponding emergency response levels according to task risk parameters of all historical tasks in the task scheduling database of the unmanned aircraft;
similarity comparison is carried out in a task scheduling database of the unmanned aircraft according to the type, the task level and the cluster number of the unmanned aircraft to be dispatched, and the historical tasks of the unmanned aircraft, the similarity of which with the type, the task level and the cluster number of the unmanned aircraft to be dispatched meets the requirement of a preset value, are obtained and serve as target tasks of the unmanned aircraft to be dispatched;
marking the emergency response level of the task according to the obtained task risk parameter corresponding to the historical task of the unmanned aerial vehicle;
and correcting the target task according to the emergency response grade, making a correction task dispatched by the unmanned aircraft, and dispatching the unmanned aircraft to perform the task.
It should be noted that, in order to prevent the task risk situation of dispatching the unmanned aerial vehicle and avoid the damage to the unmanned aerial vehicle caused by the severe working environment and conditions, a task scheduling database of the unmanned aerial vehicle is established according to the statistics of tasks and emergency measures of different types of unmanned aerial vehicles under the working conditions of different dispatching tasks, dispatching numbers and task plans, the database contains the task situations, dispatching numbers and corresponding task and emergency risk response levels in the historical tasks of various unmanned aerial vehicles, so that the optimal task and task risk situations of the unmanned aerial vehicle to be dispatched can be conveniently obtained according to the comparison of the historical database, and the task instruction is corrected according to the task risks, so that the task risks and the loss of the unmanned aerial vehicle are reduced.
According to the embodiment of the invention, the method further comprises the following steps:
setting a preset unmanned aircraft state threshold according to the unmanned aircraft state information;
the unmanned aircraft state preset threshold value is divided into threshold value ranges according to the working state of the type of unmanned aircraft in task execution;
the unmanned aircraft state preset threshold is divided into three level ranges;
acquiring real-time working state parameters of the unmanned aircraft according to real-time complete machine state information and running track data information of the dispatched unmanned aircraft;
comparing a threshold value according to the obtained real-time working state parameter of the unmanned aircraft with a preset threshold value of the state of the unmanned aircraft;
when the threshold value of the real-time working state parameter of the unmanned aircraft is in a first preset threshold value range of the state of the unmanned aircraft, the unmanned aircraft continues to execute tasks;
when the threshold value of the real-time working state parameter of the unmanned aircraft is within a second preset threshold value range of the state of the unmanned aircraft, the working state of the unmanned aircraft is interrupted and changed into an in-situ standby state, and the unmanned aircraft is waited to be overhauled;
and when the threshold value of the real-time working state parameter of the unmanned aircraft is in a third preset threshold value range of the unmanned aircraft state, terminating the working task of the unmanned aircraft and recalling.
It should be noted that, according to the real-time status information of each type of unmanned aerial vehicle when performing a task, the working status of each type of unmanned aerial vehicle is formulated and divided into threshold ranges of different levels, and according to the formulated threshold range, threshold monitoring is performed on the real-time working status information parameter of the unmanned aerial vehicle, when the real-time working status information parameter of the unmanned aerial vehicle is in different preset threshold ranges, instruction correction is performed on the unmanned aerial vehicle according to the preset threshold ranges, and according to the preset threshold ranges of the unmanned aerial vehicle, the working status of the unmanned aerial vehicle is divided into: [0, 0.5), [0.5, 0.8), [0.8,1.0], when the threshold value of the real-time working state parameter of the unmanned aircraft is in the first preset threshold value range, the working state of the unmanned aircraft is changed to the in-place standby state to wait for the unmanned aircraft to be overhauled, when the threshold value is in the second preset threshold value range, the working task of the unmanned aircraft is stopped and recalled, and when the threshold value is in the third preset threshold value range, the real-time working state parameter of the unmanned aircraft is obtained according to the dispatched real-time complete machine state information and the running track data information of the unmanned aircraft.
As shown in fig. 4, the present invention further discloses a privilege management system for an unmanned aerial vehicle, including a memory 41 and a processor 42, where the memory includes a privilege management method program of the unmanned aerial vehicle, and when executed by the processor, the privilege management method program of the unmanned aerial vehicle implements the following steps:
numbering the unmanned aircraft in the target area based on the task operation information to obtain a fleet instruction code;
extracting operation data information of the task operation information and inputting the operation data information into a preset task instruction database model to obtain instruction task data of the unmanned aircraft;
combining the command task data with the fleet command codes to obtain a task command strip of the unmanned aircraft and managing and dispatching the fleet;
and according to the task state information of the unmanned aircraft of the fleet obtained through real-time monitoring, synchronous fitting is carried out by combining the instruction task data to obtain authority matching management.
The method includes the steps of firstly numbering unmanned aircrafts in a target area according to task operation information to obtain a fleet instruction code, extracting operation data information of the task operation information, inputting the operation data information into a preset task instruction database model to obtain instruction task data of the unmanned aircrafts, combining the fleet instruction code with the instruction task data to obtain a task instruction strip of the unmanned aircrafts and managing and dispatching fleets, and synchronously fitting the task state information of the unmanned aircrafts in the fleets obtained according to real-time monitoring and the instruction task data to obtain authority matching management to conduct state monitoring and authority regulation and control on the unmanned aircrafts.
According to the embodiment of the invention, the numbering of the unmanned aerial vehicle in the target area based on the task operation information to obtain the fleet command code specifically comprises the following steps:
extracting task factors based on preset task operation information to obtain the scale grade of the unmanned aircraft fleet which needs to execute the task in the target area;
carrying out grade threshold matching according to the fleet scale grade to obtain the model and quantity expected values of the unmanned aircraft;
and numbering the unmanned aircraft in the fleet according to the unmanned aircraft fleet scale grade, the expected value and the task code to obtain a fleet instruction code.
It should be noted that, a task factor is extracted according to preset task operation information, and the scale grade of the unmanned aircraft fleet which needs to execute the task in a target area is obtained according to the task factor, because the scales of the unmanned aircraft fleet which needs to execute the task in a certain target area are different due to the difference of the task, the workload, the coverage area and the workload level, the corresponding fleet scale grade can be obtained according to the extracted task factor to meet the task requirement, and then grade threshold value matching is performed according to the fleet scale grade to obtain the unmanned aircraft model and the number expected value corresponding to the grade threshold value, for example, the grade threshold value is divided into seven grades, and the threshold value ranges are respectively [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], the different levels correspond to different models and numbers, and if the fleet scale level of the fleet a corresponds to the level threshold range of (0.51, 0.69], corresponding to the fourth level and the preset models and numbers of the unmanned aircrafts of the fourth level are respectively B-type 2, M-type 4, S-type 6 and Y-type 3, the desired values of the models and numbers of the fleet a are B2, M4, S6 and Y3, and the unmanned aircrafts in the fleet are numbered according to the fleet scale level and the desired values and the mission code to obtain a fleet command code, for example, the fleet command code of the fleet of the unmanned aircrafts of the second fleet in the fleet a is P4K6-IV-B2M4S6Y3, and the command code of the fleet is P4K6-IV-B2M4S6Y3, wherein P4K6 is the mission code;
wherein, the calculation formula of the airplane fleet scale grade is as follows:
wherein E is n For fleet size classes, S 0 Is the target area scale value, theta is the task factor,
is a preset region special coefficient, and x is a task special coefficient.
According to the embodiment of the invention, the extracting of the operation data information of the task operation information and the inputting of the operation data information into a preset task instruction database model to obtain the instruction task data of the unmanned aerial vehicle specifically comprise:
acquiring job data information of the task job information, wherein the job data information comprises job content data, job size data, job range data and job level data;
inputting the operation content data, the operation volume data, the operation range data and the operation level data into a preset task instruction database model to obtain instruction task data;
and the task instruction database model is obtained by inputting the operation content data, the operation volume data, the operation range data and the operation level data of the historical unmanned aerial vehicle into the initial task instruction database model for training.
It should be noted that, according to the task operation information, operation data information is extracted and input into a preset task instruction database model to output instruction task data, the task instruction database model requires a large amount of historical data to train, the larger the data volume is, the more accurate the result is, the preset task instruction database model in the scheme trains by taking the historical operation data information and the instruction task data as input, the accuracy of the obtained result is improved by comparing a large amount of test data with real data, and the accuracy threshold is set to 95%.
According to the embodiment of the invention, the obtaining of the unmanned aircraft task instruction strip and the management and dispatching of the fleet according to the instruction task data and the fleet instruction codes are specifically as follows:
extracting sub-instruction task data according to the instruction task data;
the sub-instruction task data correspond to route data, destination data, task node data and activity area data of unmanned aircraft in the fleet;
performing instruction marking on sub-instruction task data of the unmanned aerial vehicle according to the fleet instruction codes and generating a task instruction strip;
and carrying out scheduling control on each unmanned aircraft in the fleet according to the task instruction strip.
It should be noted that after the whole instruction task data of the fleet is obtained, the sub-instruction task data corresponding to each unmanned aerial vehicle of the fleet in the task data packet is extracted, then instruction labeling is performed on the sub-instruction task data according to the fleet instruction codes of each unmanned aerial vehicle, and task instruction bars are generated, so that each instruction task bar correspondingly contains data information of the instruction task of each unmanned aerial vehicle in the fleet, and instruction sending, information interaction and scheduling control are performed on the corresponding unmanned aerial vehicle in the fleet according to the generated task instruction bars, so that customized regulation and control of each unmanned aerial vehicle of the fleet is realized.
According to the embodiment of the invention, the task state information of the unmanned aircraft of the fleet obtained through real-time monitoring is combined with the instruction task data to perform synchronous fitting to obtain authority matching management, and the authority matching management specifically comprises the following steps:
monitoring and extracting real-time task state information of the unmanned aircraft of the fleet;
the task state information comprises dynamic course data, course target data, real-time task data and dynamic flight area data of the unmanned aerial vehicle;
performing synchronous fitting degree calculation according to the dynamic route data, the course target data, the real-time task data and the sub-instruction task data corresponding to the number of the unmanned aerial vehicle and the dynamic flight area data to obtain task similarity;
comparing instruction thresholds of all unmanned aircraft in the fleet according to the task similarity;
and marking the unmanned aircraft which does not meet the threshold comparison requirement, and modifying the command codes of the fleet.
The method includes the steps that in order to monitor task execution states of unmanned aircrafts in a fleet and match control authorities to enable the fleet to effectively execute task instruction authorities, the aircrafts need to be labeled according to the execution instruction state matching conditions of the aircrafts in the fleet, fleet instruction codes are modified according to labeled aircrafts which do not meet the task states and synchronously complete authority revision to meet effective authority control of the fleet to execute the task states, the aircrafts are guaranteed to complete execution tasks, similarity fitting calculation is conducted according to real-time task state information of the unmanned aircrafts, including dynamic course data, course target data, real-time task data and dynamic flight area data, and path data, destination data, task node data and active area data corresponding to sub-instruction task data corresponding to the numbers of the unmanned aircrafts, the similarity calculation result is a synchronous fitting degree calculation result, the similarity fitting calculation adopts Euclidean distance similarity or cosine similarity to calculate task similarity, and judges whether the current monitored unmanned aircraft execution state of the unmanned aircrafts meets preset instruction comparison requirements of a preset aircraft marking threshold or not, and the unmanned aircraft marking instructions are added to corresponding unmanned aircrafts in comparison results, and whether the unmanned aircraft editing unmanned aircrafts and other unmanned aircraft codes meet the preset instructions or not.
According to the embodiment of the invention, the method further comprises the following steps:
carrying out simulation self-inspection on each unmanned aircraft in the fleet;
acquiring energy information and state information of the unmanned aircraft;
respectively comparing the energy information and the state information with the endurance information and the airway information of the sub-instruction task data;
and if the energy information and the state information can not meet the cruising information and the route information, formatting a task instruction strip of the unmanned aerial vehicle, and synchronously correcting the command codes of the fleet.
Before the unmanned aircraft executes the mission command, the energy information and the state information of each unmanned aircraft are required to be subjected to simulated self-checking to verify whether the mission requirement is met, for example, the unmanned aircraft with the energy amount not less than 75% of the cruising information and the cruising state amount not less than 80% of the route information is permitted to execute the mission, and if the two index requirements cannot be met simultaneously, the unmanned aircraft is required to be subjected to command formatting and synchronous correction of fleet command codes, and other backup unmanned aircraft are supplemented again to fill the air.
According to the embodiment of the invention, the method further comprises the following steps:
reading the fleet running state information of the fleet in real time;
extracting instruction series according to a task instruction strip of the unmanned aircraft in the fleet in combination with a task factor;
and displaying the unmanned aircraft in the fleet in a grading manner according to the instruction series and the fleet running state information.
It should be noted that each unmanned aerial vehicle in the fleet executes task instructions and mainly comprises a plurality of task instruction bars, different instruction subtasks are executed according to different instruction bars, in order to determine the priority and the importance degree of each instruction subtask and visualize the priority and the importance degree of each instruction subtask, the order number is extracted according to task factors and the task instruction bars, and each unmanned aerial vehicle in the fleet corresponding to the order number is subjected to order grading display according to the order number and the fleet running state information, so that the order priority order is determined, the task state under the real-time instruction can be conveniently mastered, and the task instruction bars can be regulated and controlled at any time to control task instruction allocation.
According to the embodiment of the invention, the method further comprises the following steps:
establishing a task scheduling database of the unmanned aircraft according to the tasks and the task plan of the unmanned aircraft;
the task scheduling database of the unmanned aircraft comprises task grades and cluster numbers of various types of unmanned aircraft in different historical tasks and corresponding historical tasks and emergency response grades under a task plan;
generating corresponding emergency response levels according to task risk parameters of all historical tasks in the task scheduling database of the unmanned aircraft;
similarity comparison is carried out in a task scheduling database of the unmanned aircraft according to the type, the task level and the cluster number of the unmanned aircraft to be dispatched, and the historical tasks of the unmanned aircraft, the similarity of which with the type, the task level and the cluster number of the unmanned aircraft to be dispatched meets the requirement of a preset value, are obtained and serve as target tasks of the unmanned aircraft to be dispatched;
marking the emergency response level of the task according to the obtained task risk parameter corresponding to the historical task of the unmanned aerial vehicle;
and correcting the target task according to the emergency response grade, making a correction task dispatched by the unmanned aircraft, and dispatching the unmanned aircraft to perform the task.
It should be noted that, in order to prevent the task risk situation of dispatching the unmanned aerial vehicle and avoid the damage to the unmanned aerial vehicle caused by the severe working environment and conditions, a task scheduling database of the unmanned aerial vehicle is established according to the statistics of tasks and emergency measures of different types of unmanned aerial vehicles under the working conditions of different dispatching tasks, dispatching numbers and task plans, the database contains the task situations, dispatching numbers and corresponding task and emergency risk response levels in the historical tasks of various unmanned aerial vehicles, so that the optimal task and task risk situations of the unmanned aerial vehicle to be dispatched can be conveniently obtained according to the comparison of the historical database, and the task instruction is corrected according to the task risks, so that the task risks and the loss of the unmanned aerial vehicle are reduced.
According to the embodiment of the invention, the method further comprises the following steps:
setting a preset unmanned aircraft state threshold according to the unmanned aircraft state information;
the preset threshold value of the unmanned aircraft state is divided into threshold value ranges according to the working state of the unmanned aircraft in the type of task execution;
the unmanned aircraft state preset threshold is divided into three level ranges;
obtaining real-time working state parameters of the unmanned aircraft according to real-time complete machine state information and running track data information of the dispatched unmanned aircraft;
comparing a threshold value according to the obtained real-time working state parameter of the unmanned aircraft with a preset threshold value of the state of the unmanned aircraft;
when the threshold value of the real-time working state parameter of the unmanned aircraft is in a first preset threshold value range of the state of the unmanned aircraft, the unmanned aircraft continues to execute tasks;
when the threshold value of the real-time working state parameter of the unmanned aircraft is in a second preset threshold value range of the unmanned aircraft state, the working state of the unmanned aircraft is interrupted to be changed into an on-site standby state, and the unmanned aircraft is waited to be overhauled;
and when the threshold value of the real-time working state parameter of the unmanned aircraft is in a third preset threshold value range of the unmanned aircraft state, terminating the working task of the unmanned aircraft and recalling.
It should be noted that, according to the real-time status information of each type of unmanned aerial vehicle when performing a task, the working status of each type of unmanned aerial vehicle is formulated and divided into threshold ranges of different levels, and according to the formulated threshold range, threshold monitoring is performed on the real-time working status information parameter of the unmanned aerial vehicle, when the real-time working status information parameter of the unmanned aerial vehicle is in different preset threshold ranges, instruction correction is performed on the unmanned aerial vehicle according to the preset threshold ranges, and according to the preset threshold ranges of the unmanned aerial vehicle, the working status of the unmanned aerial vehicle is divided into: [0, 0.5), [0.5, 0.8), [0.8,1.0], when the threshold value of the real-time working state parameter of the unmanned aircraft is in the first preset threshold value range, the working state of the unmanned aircraft is changed to the in-place standby state to wait for the unmanned aircraft to be overhauled, when the threshold value is in the second preset threshold value range, the working task of the unmanned aircraft is stopped and recalled, and when the threshold value is in the third preset threshold value range, the real-time working state parameter of the unmanned aircraft is obtained according to the dispatched real-time complete machine state information and the running track data information of the unmanned aircraft.
A third aspect of the present invention provides a readable storage medium, where the readable storage medium includes a program for a method for authority management of an unmanned aircraft, and when the program for the method for authority management of an unmanned aircraft is executed by a processor, the method for authority management of an unmanned aircraft realizes the steps of the method for authority management of an unmanned aircraft according to any one of the above items.
The invention discloses a method, a system and a storage medium for managing and controlling the authority of an unmanned aerial vehicle, wherein the unmanned aerial vehicle in a target area is numbered based on task operation information to obtain a fleet instruction code, extract operation data information of the task operation information and input the operation data information into a preset task instruction database model to obtain instruction task data of the unmanned aerial vehicle, a mission instruction strip is obtained by combining the fleet instruction code, a scheduling fleet is managed, and synchronous fitting is carried out according to the mission state information of the unmanned aerial vehicle obtained by real-time monitoring and the instruction task data to obtain authority matching management; therefore, the crew is managed and dispatched based on the crew instruction codes and the obtained instruction task data, authority matching management is corrected through synchronous fitting of task state information and the instruction task data, and intelligent accurate management and control of task instruction authority of the unmanned aircraft are improved.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
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 one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
Claims (10)
1. A method for managing and controlling the authority of an unmanned aircraft is characterized by comprising the following steps:
numbering the unmanned aircraft in the target area based on the task operation information to obtain a fleet instruction code;
extracting operation data information of the task operation information and inputting the operation data information into a preset task instruction database model to obtain instruction task data of the unmanned aircraft;
combining the command task data with the fleet command codes to obtain a task command strip of the unmanned aircraft and managing and dispatching the fleet;
and according to the task state information of the unmanned aircraft of the fleet obtained through real-time monitoring, synchronous fitting is carried out by combining the instruction task data to obtain authority matching management.
2. The method of claim 1, wherein numbering the unmanned aerial vehicle within the target area based on the mission operations information to obtain a fleet command code comprises:
extracting task factors based on preset task operation information to obtain the scale grade of the unmanned aircraft fleet which needs to execute the task in the target area;
carrying out grade threshold matching according to the fleet scale grade to obtain the model and quantity expected values of the unmanned aircraft;
and numbering the unmanned aircraft in the fleet according to the scale grade and the expected value of the unmanned aircraft fleet and the task code to obtain a fleet instruction code.
3. The method for authority control of an unmanned aerial vehicle according to claim 2, wherein the extracting of the task data information of the task operation information and the inputting of the task data information into a preset task instruction database model to obtain instruction task data of the unmanned aerial vehicle comprises:
acquiring job data information of the task job information, wherein the job data information comprises job content data, job size data, job range data and job level data;
inputting the operation content data, the operation volume data, the operation range data and the operation level data into a preset task instruction database model to obtain instruction task data;
and the task instruction database model is input into the initial task instruction database model for training to obtain according to the operation content data, the operation volume data, the operation range data and the operation level data of the historical unmanned aircraft.
4. The method for managing and controlling authority of the unmanned aerial vehicle according to claim 3, wherein the step of obtaining a task instruction strip of the unmanned aerial vehicle and managing and dispatching the fleet according to the instruction task data and the fleet instruction codes comprises:
extracting sub-instruction task data according to the instruction task data;
the sub-instruction task data correspond to route data, destination data, task node data and activity area data of unmanned aircraft in the fleet;
performing instruction marking on sub-instruction task data of the unmanned aerial vehicle according to the fleet instruction codes and generating a task instruction strip;
and carrying out scheduling control on each unmanned aircraft in the fleet according to the task instruction strip.
5. The method for managing and controlling authority of the unmanned aerial vehicle according to claim 4, wherein the step of performing synchronous fitting according to the task state information of the unmanned aerial vehicle of the fleet obtained through real-time monitoring and in combination with the instruction task data to obtain authority matching management comprises the steps of:
monitoring and extracting real-time task state information of the unmanned aircraft of the fleet;
the task state information comprises dynamic course data, course target data, real-time task data and dynamic flight area data of the unmanned aerial vehicle;
performing synchronous fitting degree calculation according to the dynamic route data, the course target data, the real-time task data and the sub-instruction task data corresponding to the number of the unmanned aerial vehicle and the dynamic flight area data to obtain task similarity;
comparing instruction thresholds of all unmanned aircraft in the fleet according to the task similarity;
and marking the unmanned aircraft which does not meet the threshold comparison requirement, and modifying the command codes of the fleet.
6. The method of claim 1, further comprising:
carrying out simulation self-inspection on each unmanned aircraft in the fleet;
acquiring energy information and state information of the unmanned aircraft;
respectively comparing the energy information and the state information with the endurance information and the airway information of the sub-instruction task data;
and if the energy information and the state information can not meet the cruising information and the route information, formatting a task instruction strip of the unmanned aerial vehicle, and synchronously correcting the command codes of the fleet.
7. The method of claim 1, further comprising:
reading the fleet running state information of the fleet in real time;
extracting instruction series according to a task instruction bar of the unmanned aircraft in the fleet in combination with a task factor;
and displaying the unmanned aircraft in the fleet in a grading manner according to the instruction series and the fleet running state information.
8. A rights management system for an unmanned aerial vehicle, the system comprising: a memory including a program of a method for authority management of an unmanned aircraft, and a processor, wherein the program of the method for authority management of an unmanned aircraft when executed by the processor implements the steps of:
numbering the unmanned aircraft in the target area based on the task operation information to obtain a fleet instruction code;
extracting operation data information of the task operation information and inputting the operation data information into a preset task instruction database model to obtain instruction task data of the unmanned aircraft;
combining the command task data with the fleet command codes to obtain a task command strip of the unmanned aircraft and managing and dispatching the fleet;
and performing synchronous fitting by combining the instruction task data according to the task state information of the unmanned aircraft of the fleet, which is obtained by real-time monitoring, so as to obtain authority matching management.
9. The jurisdictional management and control system for unmanned aerial vehicles according to claim 8, wherein the numbering of unmanned aerial vehicles within a target area based on mission operations information results in a fleet command code comprising:
extracting task factors based on preset task operation information to obtain the scale grade of the unmanned aircraft fleet which needs to execute the task in the target area;
carrying out grade threshold matching according to the fleet scale grade to obtain the model and quantity expected values of the unmanned aircraft;
and numbering the unmanned aircraft in the fleet according to the unmanned aircraft fleet scale grade, the expected value and the task code to obtain a fleet instruction code.
10. A computer-readable storage medium, characterized in that a program of a method of authority management for an unmanned aerial vehicle is included in the computer-readable storage medium, which program, when executed by a processor, carries out the steps of the method of authority management for an unmanned aerial vehicle according to any one of claims 1 to 7.
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| PCT/CN2023/134145 WO2024114543A1 (en) | 2022-11-28 | 2023-11-24 | Unmanned aerial vehicle permission management method and system, and storage medium |
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| CN112433539A (en) * | 2020-11-27 | 2021-03-02 | 佛山市墨纳森智能科技有限公司 | Unmanned aerial vehicle scheduling method and system for processing high-speed traffic accidents and readable storage medium |
| CN114298552A (en) * | 2021-12-28 | 2022-04-08 | 广东电网有限责任公司 | Unmanned aerial vehicle multi-machine cooperative operation management method and system and readable storage medium |
| CN115829260A (en) * | 2022-11-28 | 2023-03-21 | 亿航智能设备（广州）有限公司 | Authority management and control method and system of unmanned aircraft and storage medium |
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