CN115357041A - Unmanned aerial vehicle group monitoring method and system based on 5G mobile networking and electronic equipment - Google Patents
Unmanned aerial vehicle group monitoring method and system based on 5G mobile networking and electronic equipment Download PDFInfo
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Abstract
The invention provides a 5G mobile networking-based unmanned aerial vehicle group monitoring method, a system and electronic equipment, wherein the monitoring method comprises the following steps: determining the number of the unmanned aerial vehicles according to the area of the target area, the performance parameters of the unmanned aerial vehicles and the modeling precision; determining the flight track of each unmanned aerial vehicle according to the modeling requirement of the target area; determining the moving track of the 5G base station vehicle according to the terrain, the road condition and the flight track of the target area; controlling the unmanned aerial vehicle to fly along the flight track, shooting images and sending image data; and controlling the 5G base station vehicle to move along the moving track, receiving the image data and carrying out three-dimensional modeling through a vehicle-mounted cluster server. According to the unmanned aerial vehicle group monitoring method based on the 5G mobile networking, the limitation that offline summarized data are needed in the traditional mode is eliminated, the inspection efficiency and the three-dimensional modeling efficiency are greatly improved, and the unmanned aerial vehicle group monitoring method is suitable for various scenes and has strong practicability and high economic value.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle shooting, in particular to an unmanned aerial vehicle group monitoring method based on 5G mobile networking.
Background
Currently, unmanned aerial vehicle inspection technology has been applied to a plurality of aspects such as chemical production safety, forest fire alarm, circuit line inspection, homeland surveying and mapping. Among them, it is common practice to three-dimensionally model a target region using oblique photography. Compared with the traditional method, the technology can obtain the clear three-dimensional image of the target area, and greatly improves the definition of the image and the three-dimensional modeling precision.
But single unmanned aerial vehicle need shoot many times, charge many times when patrolling, and the low-range operation of flying hand to need off-line processing data, not only work efficiency is low, is not convenient for moreover to the quick accurate inspection in target area, has improved the inspection cost.
Disclosure of Invention
The technical problems to be solved by the embodiment of the invention are that the single unmanned aerial vehicle has low efficiency of target area shape inspection, poor three-dimensional modeling precision and high cost.
In view of this, the invention provides an unmanned aerial vehicle group monitoring method based on 5G mobile networking.
The invention also provides a 5G mobile networking-based unmanned aerial vehicle group monitoring system.
The invention also provides electronic equipment.
The invention also provides a readable storage medium.
In order to solve the technical problems, the invention adopts the following technical scheme:
according to the embodiment of the first aspect of the invention, the method for monitoring the unmanned aerial vehicle cluster based on the 5G mobile networking comprises the following steps:
determining the number of the unmanned aerial vehicles according to the area of the target area, the performance parameters of the unmanned aerial vehicles and the modeling precision;
determining the flight track of each unmanned aerial vehicle according to the modeling requirement of the target area;
determining the movement track of the 5G base station vehicle according to the terrain, the road condition and the flight track of the target area;
the unmanned aerial vehicle flies along the flight track, shoots images and sends image data;
and the 5G base station vehicle moves along the moving track, receives the image data and carries out three-dimensional modeling through a vehicle-mounted cluster server.
The unmanned aerial vehicle group monitoring method based on 5G mobile networking, provided by the embodiment of the invention, can also have the following technical characteristics.
Further, the calculation formula for determining the number of the unmanned aerial vehicles according to the area of the target area, the performance parameters of the unmanned aerial vehicles and the modeling precision is as follows:
wherein S is the area of the target area, T is the endurance time of the unmanned aerial vehicle, V is the flight speed of the unmanned aerial vehicle, X is the number of single-machine cameras, P is the three-dimensional modeling precision, and epsilon, alpha, beta and gamma are respectively adjustment coefficients.
Further, the determining the flight trajectory of each unmanned aerial vehicle according to the modeling requirements of the target area comprises the following steps:
selecting space three-dimensional coordinate m of shooting point k ＝(x k ,y k ,z k ) Forming a shooting point coordinate set M = { M = { (M) } 1 ,m 2 ,...,m K (k is a natural number);
by optimizing
The model allocates different shooting points to different unmanned aerial vehicles;
wherein,
the shooting track formed by shooting points in the set M is shown, and the C is the time consumed by the unmanned aerial vehicle which completes the shooting task at the latest in the N unmanned aerial vehicles.
Further, the step of determining the movement track of the 5G base station vehicle according to the terrain of the target area, the road condition and the flight track comprises the following steps:
the method comprises the steps of carrying out grid division on roads in a target area where 5G base station vehicles can run, numbering each grid, and forming a position set L = { L = { (L) } 1 ,l 2 ,...,l j ,...,l J }；
Every preset time interval, acquiring the position coordinates of each unmanned aerial vehicle, and calculating the position l of each unmanned aerial vehicle j The communication rate of (c);
and calculating the action track of the 5G base station vehicle by a reinforcement learning algorithm.
Further, the size of the mesh is 20m × 20m.
Further, the communication rate calculation formula is:
D nj ＝2*B*log(1+SNR nj )
wherein the SNR nj For aircraft n to gridded locations l j Signal to noise ratio of D nj B is the transmission bandwidth for the communication rate.
Further, the reward function calculation value of the reinforcement learning algorithm is: in the whole fleet patrol process, the sum of the communication rates of any unmanned aerial vehicle and the 5G base station vehicle at any time is maximum;
the constraint conditions of the reinforcement learning algorithm are as follows: and the communication speed between any unmanned aerial vehicle and the 5G base station vehicle at any moment is not lower than 2Mbit/s.
According to the embodiment of the second aspect of the invention, the unmanned aerial vehicle group monitoring system based on the 5G mobile networking comprises:
the determining module is used for determining the number of the unmanned aerial vehicles according to the area of the target area, the performance parameters of the unmanned aerial vehicles and the modeling precision; determining the flight track of each unmanned aerial vehicle according to the modeling requirement of the target area; determining the movement track of the 5G base station vehicle according to the terrain, the road condition and the flight track of the target area;
the first execution module controls the unmanned aerial vehicle to fly along the flight track, controls the unmanned aerial vehicle to shoot images and sends image data;
and the second execution module controls the 5G base station vehicle to move along the movement track, and controls the 5G base station vehicle to receive the image data and perform three-dimensional modeling through a vehicle-mounted cluster server.
An electronic device according to a third aspect of the invention, comprising: the system comprises a processor, a communication interface, a communication bus, a memory and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the computer program to realize the unmanned aerial vehicle group monitoring method based on the 5G mobile networking according to the embodiment.
According to a fourth aspect of the present invention, a readable storage medium stores a program, which when executed by a processor, implements the method for monitoring a 5G mobile networking-based drone group according to the above embodiment.
The technical scheme of the invention at least has the following technical effects:
according to the unmanned aerial vehicle group monitoring method based on the 5G mobile networking, the unmanned aerial vehicle group is simultaneously operated in parallel, meanwhile, the 5G communication mobile networking mode is adopted, the shot data are transmitted and summarized in real time, the limitation that the data need to be summarized offline in the traditional mode is eliminated, the patrol efficiency and the three-dimensional modeling efficiency are greatly improved, the method is suitable for various scenes, and is high in practicability and economic value.
Drawings
FIG. 1 is a flow chart of a method for monitoring a fleet of 5G mobile networks according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a 5G mobile networking-based unmanned aerial vehicle fleet monitoring system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the invention.
Reference numerals
The unmanned aerial vehicle group detection system 200 based on 5G mobile networking; a determination module 201; a first execution module 202; a second execution module 203; a processor 301; a communication interface 302; a memory 303; a communication bus 304.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.
The following first describes in detail the unmanned aerial vehicle group monitoring method based on 5G mobile networking according to the embodiment of the present invention with reference to the accompanying drawings.
As shown in fig. 1, the method for monitoring a fleet of 5G-based mobile networks according to an embodiment of the present invention includes the following steps:
s101, determining the number of the unmanned aerial vehicles according to the area of a target area, the performance parameters of the unmanned aerial vehicles and the modeling precision;
in other words, the size of the working crew is determined according to the requirements such as the size of the area where oblique photography is required.
S102, determining the flight track of each unmanned aerial vehicle according to the modeling requirement of the target area;
specifically, the flight trajectory of each unmanned aerial vehicle can be determined through the Xinjiang intelligent map software according to the requirements of oblique photography three-dimensional modeling, namely, each unmanned aerial vehicle is responsible for shooting work of which shooting points.
S103, determining the movement track of the 5G base station vehicle according to the terrain, road conditions and flight track of the target area;
that is to say, according to the topography, the road of target area, and unmanned aerial vehicle's flight track, confirm the position and the removal orbit of 5G base station car to ensure can serve the transmission of the aerial 5G communication data of many unmanned aerial vehicles of corresponding period.
S104, the unmanned aerial vehicle flies along the flight track, shoots images and sends image data;
and S105, moving the 5G base station vehicle along the moving track, receiving the image data and carrying out three-dimensional modeling through a vehicle-mounted cluster server.
Specifically, the unmanned aerial vehicle flies according to a flight track, a 5G base station vehicle moves along the movement track, the unmanned aerial vehicle shoots pictures, 4S is cached every time, the cached image data are transmitted to the base station, the base station receives the image data transmitted by the unmanned aerial vehicles and transmits the image data to a vehicle-mounted cluster server, the server runs the three-dimensional modeling software of the map, the modeling software is used for carrying out three-dimensional modeling on the shot target, meanwhile, with the advancing of the shooting process and the input of new data, the subsequent targets are modeled, and finally the whole model construction of the whole target area is completed.
Therefore, according to the unmanned aerial vehicle group monitoring method based on the 5G mobile networking, the unmanned aerial vehicle group is simultaneously operated in parallel, and meanwhile, the 5G communication mobile networking mode is adopted to transmit and gather shot data in real time, so that the limitation that the traditional mode needs offline summarized data is eliminated, the patrol efficiency and the three-dimensional modeling efficiency are greatly improved, and the unmanned aerial vehicle group monitoring method based on the 5G mobile networking is suitable for various scenes, high in practicability and high in economic value.
According to one embodiment of the invention, the calculation formula for determining the number of the unmanned aerial vehicles according to the area of the target area, the performance parameters of the unmanned aerial vehicles and the modeling precision is as follows:
s is the area of a target area, T is the duration of the unmanned aerial vehicle, V is the flight speed of the unmanned aerial vehicle, X is the number of single-machine cameras, P is the three-dimensional modeling precision, 3cm is the number, epsilon, alpha, beta and gamma are respectively adjustment coefficients, the specific determination of the adjustment coefficients mainly depends on the empirical setting, wherein 3cm is 3 cm.
Preferably, the flight trajectory of each drone is determined according to the modeling requirements of the target area, comprising the following steps:
selecting space three-dimensional coordinate m of shooting point k ＝(x k ,y k ,z k ) Forming a shooting point coordinate set M = { M = { (M) } 1 ,m 2 ,...,m K (k is a natural number);
by optimizing
The model allocates different shooting points to different unmanned aerial vehicles;
wherein,
the unmanned aerial vehicle is a shooting track formed by shooting points in the set M, and the C is the time consumed by the unmanned aerial vehicle which completes a shooting task at the latest in the N unmanned aerial vehicles.
The model is solved by adopting a standard ant colony algorithm, and a patrol shooting point set of each airplane, namely a patrol track of each airplane is obtained.
According to one embodiment of the invention, the step of determining the movement track of the 5G base station vehicle according to the terrain, the road condition and the flight track of the target area comprises the following steps:
the method comprises the steps of carrying out grid division on roads in a target area where 5G base station vehicles can run, numbering each grid, and forming a position set L = { L = { (L) } 1 ,l 2 ,...,l j ,...,l J And f, wherein the set L represents positions which the 5G base stations may pass through.
Every preset time interval, acquiring the position coordinates of each unmanned aerial vehicle, and calculating the position l of each unmanned aerial vehicle j The communication rate of (c);
and calculating the action track of the 5G base station vehicle through a reinforcement learning algorithm, wherein the 5G base station vehicle runs in the grid according to a certain sequence.
Preferably, the size of the mesh is 20m × 20m.
Optionally, the base station takes 4 seconds as a time interval unit, and the unmanned aerial vehicle runs to shoot a point to capture the position, so that the timeliness of data processing is improved. For each captured unmanned aerial vehicle aerial deployment form, calculating the possible positions l from the unmanned aerial vehicle n where each shooting point is located to gridding j The communication rate of the 5G base station, the calculation formula of the communication rate is:
D nj ＝2*B*log(1+SNR nj )
wherein the SNR nj From n to a gridded possible base station location l for an aircraft j Signal to noise ratio of D nj For the communication rate obtained by importing the terrain simulation of the target area by using the winprep electromagnetic simulation software, B is the transmission bandwidth
In one embodiment of the present invention, the reward function calculation value of the reinforcement learning algorithm is: in the whole fleet patrol process, the sum of the communication rates of any unmanned aerial vehicle and the 5G base station vehicle at any time is maximum;
the constraint conditions of the reinforcement learning algorithm are as follows: the communication speed between any unmanned aerial vehicle and the 5G base station vehicle at any moment is not lower than 2Mbit/s.
In summary, according to the unmanned aerial vehicle group monitoring method based on the 5G mobile networking, the unmanned aerial vehicle group is simultaneously operated in parallel, and meanwhile, the 5G communication mobile networking mode is adopted to transmit and gather shot data in real time, so that the limitation that the traditional mode needs offline summarized data is eliminated, the patrol efficiency and the three-dimensional modeling efficiency are greatly improved, and the unmanned aerial vehicle group monitoring method based on the 5G mobile networking is suitable for various scenes, and is high in practicability and high in economic value.
In another embodiment provided by the present invention, as shown in fig. 2, there is further provided an unmanned aerial vehicle group monitoring system 200 based on 5G mobile networking, including: the determining module 201 is used for determining the number of the unmanned aerial vehicles according to the area of the target area, the performance parameters of the unmanned aerial vehicles and the modeling precision by the determining module 201; determining the flight track of each unmanned aerial vehicle according to the modeling requirement of the target area; determining the movement track of the 5G base station vehicle according to the terrain, the road condition and the flight track of the target area;
the first execution module 202, the first execution module 202 controls the unmanned aerial vehicle to fly along the flight trajectory, controls the unmanned aerial vehicle to shoot images and sends image data;
and the second execution module 203 controls the 5G base station vehicle to move along the moving track, controls the 5G base station vehicle to receive the image data and carries out three-dimensional modeling through a vehicle-mounted cluster server.
According to another embodiment of the present invention, there is also provided an electronic device, as shown in fig. 3, including a processor 301, a communication interface 302, a communication bus 304 and a memory 303, wherein the processor 301, the communication interface 302 and the storage interact with each other through the communication bus 304.
The memory 303 is used for storing computer programs;
a processor 301 for executing the program stored in the memory 303, the computer program, when executed by the processor 301, for
Determining the number of the unmanned aerial vehicles according to the area of the target area, the performance parameters of the unmanned aerial vehicles and the modeling precision;
determining the flight track of each unmanned aerial vehicle according to the modeling requirement of the target area;
determining the movement track of the 5G base station vehicle according to the terrain, the road condition and the flight track of the target area;
controlling the unmanned aerial vehicle to fly along the flight track, shooting images and sending image data;
and controlling the 5G base station vehicle to move along the moving track, receiving the image data and carrying out three-dimensional modeling through a vehicle-mounted cluster server.
When the calculator program is executed by the processor 301, the calculation formula for determining the number of drones according to the area of the target area, the performance parameters of the drones, and the modeling precision is as follows:
wherein S is the area of the target area, T is the endurance time of the unmanned aerial vehicle, V is the flight speed of the unmanned aerial vehicle, X is the number of single-machine cameras, P is the three-dimensional modeling precision, and epsilon, alpha, beta and gamma are respectively adjustment coefficients.
When the computer program is executed by the processor 301, the determining the flight trajectory of each drone according to the modeling requirements of the target area includes the following steps:
selecting space three-dimensional coordinate m of shooting point k ＝(x k ,y k ,z k ) Forming a shooting point coordinate set M = { M = { (M) } 1 ,m 2 ,...,m K }, (k is a natural number);
by optimizing
The model allocates different shooting points to different unmanned aerial vehicles;
wherein,
the shooting track formed by shooting points in the set M is shown, and the C is the time consumed by the unmanned aerial vehicle which completes the shooting task at the latest in the N unmanned aerial vehicles.
When the computer program is executed by the processor 301, the determining the moving track of the 5G base station vehicle according to the terrain of the target area, the road condition and the flight track comprises the following steps:
the method comprises the steps of carrying out grid division on roads in a target area where 5G base station vehicles can run, numbering each grid, and forming a position set L = { L = { (L) } 1 ,l 2 ,...,l j ,...,l J }；
Every preset time interval, acquiring the position coordinates of each unmanned aerial vehicle, and calculating the position l of each unmanned aerial vehicle j The communication rate of (c);
and calculating the action track of the 5G base station vehicle by a reinforcement learning algorithm.
The size of the grid is 20m × 20m when the calculator program is executed by the processor 301.
When the calculator program is executed by the processor 301, the communication rate calculation formula is:
D nj ＝2*B*log(1+SNR nj )
wherein the SNR nj From n to a gridded possible base station location l for an aircraft j Signal to noise ratio of D nj B is the transmission bandwidth for the communication rate.
When the computer program is executed by the processor 301, the reward function of the reinforcement learning algorithm is calculated as: in the whole fleet patrol process, the sum of the communication rates of any unmanned aerial vehicle and the 5G base station vehicle at any time is the maximum;
the constraint conditions of the reinforcement learning algorithm are as follows: and the communication speed between any unmanned aerial vehicle and the 5G base station vehicle at any moment is not lower than 2Mbit/s.
The communication bus 304 mentioned above for the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus 304 may be divided into an address bus, a data bus, a control bus, and the like. For ease of identification, the figures are shown with a single thick line, but do not represent only a single bus or a single data type.
The communication interface 302 is used for communication between the above-described terminal and other devices.
The Memory 303 may include a Random Access Memory 303 (RAM), or may include a non-volatile Memory 303 (e.g., at least one disk Memory 303). Optionally, the memory 303 may also be at least one storage device located remotely from the processor 301.
The Processor 301 may be a general-purpose Processor 301, and includes a Central Processing Unit 301 (CPU), a Network Processor 301 (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components.
In another embodiment of the present invention, a computer-readable storage medium is further provided, where instructions are stored in the computer-readable storage medium, and when the instructions are executed on a computer, the computer is caused to execute the method for monitoring an unmanned aerial vehicle fleet based on 5G mobile networking according to any one of the foregoing embodiments.
In yet another embodiment provided by the present invention, there is also provided a computer program product containing instructions, which when run on a computer, causes the computer to execute the 5G mobility networking based drone swarm monitoring method described in any of the above embodiments.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), among others. Other structures and operations of the vehicle according to the embodiment of the present invention will be understood and readily implemented by those skilled in the art, and thus will not be described in detail.
Unless defined otherwise, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs. The use of "first," "second," and similar terms in the present application do not denote any order, quantity, or importance, but rather the terms are used to distinguish one element from another. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships are changed accordingly.
While the foregoing is directed to the preferred embodiment of the present invention, it will be appreciated by those skilled in the art that various changes and modifications may be made therein without departing from the principles of the invention as set forth in the appended claims.
Claims (10)
1. A5G mobile networking-based unmanned aerial vehicle group monitoring method is characterized by comprising the following steps:
determining the number of the unmanned aerial vehicles according to the area of the target area, the performance parameters of the unmanned aerial vehicles and the modeling precision;
determining the flight track of each unmanned aerial vehicle according to the modeling requirement of the target area;
determining the movement track of the 5G base station vehicle according to the terrain, the road condition and the flight track of the target area;
controlling the unmanned aerial vehicle to fly along the flight track, shooting images and sending image data;
and controlling the 5G base station vehicle to move along the moving track, receiving the image data and carrying out three-dimensional modeling through a vehicle-mounted cluster server.
2. The unmanned aerial vehicle group monitoring method based on 5G mobile networking according to claim 1, wherein the calculation formula for determining the number of unmanned aerial vehicles according to the area of the target area, the performance parameters of the unmanned aerial vehicles and the modeling precision is as follows:
wherein S is the area of the target area, T is the endurance time of the unmanned aerial vehicle, V is the flight speed of the unmanned aerial vehicle, X is the number of single-machine cameras, P is the three-dimensional modeling precision, and epsilon, alpha, beta and gamma are respectively adjustment coefficients.
3. The unmanned aerial vehicle fleet monitoring method according to claim 1, wherein determining the flight trajectory of each unmanned aerial vehicle according to the modeling requirements of the target area comprises the steps of:
selecting space three-dimensional coordinate m of shooting point k ＝(x k ,y k ,z k ) Forming a coordinate set of the shooting points
By optimizing
The model allocates different shooting points to different unmanned aerial vehicles;
wherein,
the unmanned aerial vehicle is a shooting track formed by shooting points in the set M, and the C is the time consumed by the unmanned aerial vehicle which completes a shooting task at the latest in the N unmanned aerial vehicles.
4. The unmanned aerial vehicle group monitoring method based on 5G mobile networking according to claim 3, wherein the step of determining the movement track of the 5G base station vehicle according to the terrain of the target area, the road condition and the flight track comprises the following steps:
the method comprises the steps of carrying out grid division on roads in a target area where 5G base station vehicles can run, numbering each grid, and forming a position set L = { L = { (L) } 1 ,l 2 ,...,l j ,...,l J }；
Every preset time interval, acquiring the position coordinates of each unmanned aerial vehicle, and calculating the arrival position l of each unmanned aerial vehicle j The communication rate of (c);
and calculating the action track of the 5G base station vehicle by a reinforcement learning algorithm.
5. The drone swarm monitoring method based on 5G mobile networking of claim 4, wherein the size of the grid is 20m × 20m.
6. The unmanned aerial vehicle group monitoring method based on 5G mobile networking of claim 4, wherein the communication rate calculation formula is as follows:
D nj ＝2*B*log(1+SNR nj )
wherein the SNR nj For aircraft n to gridded locations l j Signal to noise ratio of D nj For communication rate, B is transmission bandAnd (4) wide.
7. The unmanned aerial vehicle fleet monitoring method according to claim 4, wherein said reinforcement learning algorithm comprises a return function calculation value: in the whole fleet patrol process, the sum of the communication rates of any unmanned aerial vehicle and the 5G base station vehicle at any time is maximum;
the constraint conditions of the reinforcement learning algorithm are as follows: the communication speed between any unmanned aerial vehicle and the 5G base station vehicle at any moment is not lower than 2Mbit/s.
8. The utility model provides a unmanned aerial vehicle crowd monitoring system based on mobile 5G networking which characterized in that includes: the determining module is used for determining the number of the unmanned aerial vehicles according to the area of the target area, the performance parameters of the unmanned aerial vehicles and the modeling precision; determining the flight track of each unmanned aerial vehicle according to the modeling requirement of the target area; determining the movement track of the 5G base station vehicle according to the terrain, the road condition and the flight track of the target area;
the first execution module controls the unmanned aerial vehicle to fly along the flight track, controls the unmanned aerial vehicle to shoot images and sends image data;
and the second execution module controls the 5G base station vehicle to move along the movement track, and controls the 5G base station vehicle to receive the image data and perform three-dimensional modeling through a vehicle-mounted cluster server.
9. An electronic device, comprising: a processor, a communication interface, a communication bus, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for monitoring the drone swarm based on the 5G mobile networking according to any of claims 1 to 7.
10. A readable storage medium, wherein the readable storage medium stores thereon a program, which when executed by a processor implements the 5G mobility networking based drone swarm monitoring method according to any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115766789A (en) * | 2022-11-21 | 2023-03-07 | 北京思朗东芯科技有限责任公司 | Data processing method and device based on unmanned aerial vehicle cluster |
| CN117062006A (en) * | 2023-08-11 | 2023-11-14 | 重庆兰空无人机技术有限公司 | Network-connected unmanned aerial vehicle identification and control method, system, equipment and storage medium |
| CN117330714A (en) * | 2023-12-01 | 2024-01-02 | 江苏新睿清智科技有限公司 | Regional environment monitoring and early warning system and method based on big data |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115766789A (en) * | 2022-11-21 | 2023-03-07 | 北京思朗东芯科技有限责任公司 | Data processing method and device based on unmanned aerial vehicle cluster |
| CN115766789B (en) * | 2022-11-21 | 2023-09-12 | 北京思朗东芯科技有限责任公司 | Unmanned aerial vehicle cluster-based data processing method and device, storage medium and electronic equipment |
| CN117062006A (en) * | 2023-08-11 | 2023-11-14 | 重庆兰空无人机技术有限公司 | Network-connected unmanned aerial vehicle identification and control method, system, equipment and storage medium |
| CN117062006B (en) * | 2023-08-11 | 2024-04-26 | 重庆兰空无人机技术有限公司 | Network-connected unmanned aerial vehicle identification and control method, system, equipment and storage medium |
| CN117330714A (en) * | 2023-12-01 | 2024-01-02 | 江苏新睿清智科技有限公司 | Regional environment monitoring and early warning system and method based on big data |
| CN117330714B (en) * | 2023-12-01 | 2024-02-13 | 江苏新睿清智科技有限公司 | Regional environment monitoring and early warning system and method based on big data |
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