Cloud wing 6G aerial power transmission unmanned aerial vehicle

Abstract

The invention discloses a cloud wing 6G air power transmission unmanned aerial vehicle which comprises an unmanned aerial vehicle body and a charging device, the unmanned aerial vehicle body is connected with the charging device through an electromagnet, and a digital twinborn computing system is arranged on the unmanned aerial vehicle body. Operation interruption caused by the charging problem is reduced, the operation efficiency is improved, and meanwhile technical support is provided for application of the unmanned aerial vehicle technology in more fields.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and more specifically to a CloudWing 6G aerial power transmission UAV. Background Technology

[0002] Traditionally, aerial work platforms are charged by directly connecting a charging cable to the device's charging port. Safety is a core concern. Wired charging carries a small risk of leakage. Since it requires human contact, improper use of the charger or other methods can lead to electric shock. Because the current in wired charging is not converted, it generates some radiation—general magnetic radiation—which is low in current and voltage and has negligible impact on the human body. Aside from the heat generated by the charger and battery during charging, wired charging generally produces very little heat, with negligible impact on the equipment. However, with the advent of fast charging and high-current charging technologies, the heat generated by wired charging is a significant concern. Fast charging and high-current charging place specific technical requirements on the cables, leading to overheating issues. Electromagnetic induction wireless charging technology, on the other hand, is already in mass production and has been proven safe and market-tested. Its production cost is lower than other technologies, and it provides an effective solution to the heat generation problem during charging. Wireless charging requires magnetic field conversion. Under an amplified magnetic field, prolonged close proximity (within 20cm) can still have some impact on the human body. However, since the charging process takes place in the air, far above the ground (greater than 20cm), the magnetic field weakens significantly, theoretically eliminating any radiation impact. Wireless charging eliminates the need for charging cables, thus avoiding the heat issues associated with wired charging. Applying it to high-altitude operations could eliminate the risks of wired charging, making the charging process safer and more efficient. Summary of the Invention

[0003] (a) Technical problems to be solved

[0004] To overcome the shortcomings of existing technologies, a Cloud Wing 6G aerial power transmission drone is proposed to solve the problems mentioned in the background technology.

[0005] (II) Technical Solution

[0006] This invention is achieved through the following technical solution: This invention proposes a Cloud Wing 6G aerial power transmission drone, characterized in that it includes a drone body and a charging device. The drone body is connected to the charging device via electromagnetic attraction. A digital twin computing system is installed on the drone body, and the algorithm of the digital twin computing system is as follows:

[0007] First, the UAV receives the mission signal and obtains the specific location of the mission target (1,1,1). During the flight, the real-time position of the UAV is represented as (x,y,z). The real-time flight speed of the UAV is 0.k. The relevant drag coefficient related to the speed during flight is ; the UAV flight energy consumption is ; γ is the correlation coefficient of the energy consumed by the UAV when hovering. Input the specific location of the mission target, and then calculate H route simulations according to the actual environmental conditions, and simulate flight time; H=50 (1)3 The distance of the UAV flight in the X direction is x, the distance in the Y direction is y, and the distance in the Z direction is z; the flight time in the X direction, the flight time in the Y direction is Ty, and the flight time in the Z direction is ; m is the weight of the UAV when carrying the charging equipment; the flight time T is: T = Ty ==kVn 2 +γmgZn When hovering, =0 The total energy consumption of the UAV flight is: E =0 (2) (3) (4) (5) The optimal route is obtained by comparing the energy consumption of the H route simulations.

[0008] (III) Beneficial Effects

[0009] Compared with the prior art, the present invention has the following advantages:

[0010] This product utilizes a new algorithm developed using digital twins, enabling the system to schedule charging tasks based on real-time monitoring of the remaining battery power of the task sender when receiving multiple tasks. Similarly, when receiving multiple charging tasks, it can prioritize them according to their urgency and select the optimal flight path and charging method based on flight time and the battery consumption of the equipment to be charged. The system generates the optimal solution and transmits the charging task signal to the drone. The drone accepts the charging task and, according to its plan, carries the "power bank" to the location of the equipment to complete the charging task.

[0011] Furthermore, this product uses a specific current to drive a coil, generating a strong magnetic force that allows it to attach the "power bank" to the drone. This ensures successful transport of the "charging pack" to the charging equipment even in harsh environments that are difficult to access manually, such as high altitudes, cliffs, and tunnels. Similarly, the drone is coupled and aligned with the device generating the corresponding magnetic field. The magnetic charging equipment prepares for the release of the "charging pack." When the drone receives the release signal, it stops supplying power to the coil, causing the coil to stop generating the magnetic field. At this point, the drone loses its attraction to the "power bank," and the "charging pack" is then attracted to the device. The charging process is completed through contact, and the drone returns after completing its charging mission.

[0012] The drone charging technology of this invention, through the integration and innovation of the aforementioned key technologies, not only solves the shortcomings of existing technologies in high-altitude and long-distance charging, but also provides strong technical support for the application of drone technology in more fields. Through practical implementation and field experiments, this invention is expected to create an innovative drone charging mode, providing strong technical support for the development and application of drone technology. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0014] This invention proposes a CloudWing 6G aerial power transmission drone, characterized by: a drone body and a charging device, the drone body being connected to the charging device via electromagnetic attraction, and a digital twin computing system being installed on the drone body, the algorithm of which is as follows:

[0015] First, the UAV receives the mission signal and obtains the specific location of the mission target (1,1,1). During the flight, the real-time position of the UAV is represented as (x,y,z). The real-time flight speed of the UAV is 0.k. The relevant drag coefficient related to the speed during flight is γ, which is the energy consumption of the UAV during flight. The energy consumption of the UAV during hovering is the correlation coefficient. Input the specific location of the mission target, and then calculate H route simulations according to the actual environmental conditions, and simulate flight time. H=50 (1)3 The distance of the UAV in the X direction is x, the distance in the Y direction is y, and the distance in the Z direction is z. The flight time in the X direction, the flight time in the Y direction is Ty, and the flight time in the Z direction is m. m is the weight of the UAV when carrying the charging equipment. The flight time T is: T = Ty ==kVn 2 +γmgZn When hovering, =0 The total energy consumption of the UAV during flight is: E =0 (2) (3) (4) (5) The optimal route is obtained by comparing the energy consumption of the H route simulations.

[0016] The invention mentions a CloudWing 6G aerial power transmission drone that uses a new 6G technology—digital twin—to improve magnetic attraction strength.

[0017] First, the UAV receives the mission signal and obtains the specific location of the mission target (1,1,1). During the flight, the real-time position of the UAV is represented as (x,y,z). The real-time flight speed of the UAV is 0. k is the drag coefficient related to speed during flight. γ is the energy consumption of the UAV flight. γ is the correlation coefficient of the energy consumed by the UAV when hovering. Input the specific location of the mission target, and then calculate H route simulations according to the actual environmental conditions, and simulate flight time. H=50(1) 3 The distance of the UAV flight in the X direction is x, the distance in the Y direction is y, and the distance in the Z direction is z. The flight time in the X direction, the flight time in the Y direction is Ty, and the flight time in the Z direction is m. m is the weight of the UAV when carrying the charging equipment. The flight time T is: T =Ty==kVn 2 +γmgZn When hovering, =0 The total energy consumption of the UAV flight is: E =0 (2) (3) (4) (5) Based on the comparison of the energy consumption of the H route simulations, the optimal route is obtained: min E (6) The second is electromagnetic attraction technology. By equipping a drone with specific devices, the drone generates current that drives a coil to produce a strong magnetic force, allowing the charging device to be attracted to the drone. This ensures successful transport of the charging device to the location of the equipment, even in harsh environments difficult to access manually, such as high altitudes, cliffs, and tunnels. Similarly, the device generating the corresponding magnetic field couples with the drone. A magnetically attracted charging receiver prepares for the release of the charging device. When the drone receives the release signal, it stops supplying power to the coil, which stops generating the magnetic field. At this point, the drone loses its attraction to the charging device, which is then attracted to the device. The charging process is completed through contact, and the drone returns after charging. Algorithm 2 is as follows: 0 is the drone's initial battery level, ε is the contact current loss during charging, 1 is the drone's charging time, 1 is the drone's real-time battery level, 2 is the energy loss during the drone's electromagnetization, I is the current required for magnetic attraction, 2 is the time for current generation, R is the coil resistance, 4 is the total energy consumed by the drone, 5 is the energy required for the drone to return, and 6 is the energy consumed by the drone while hovering to complete charging. 26 =0 γmgZn 1=0-2-E-6-ε2=I2R24=2+E+6+ ε and the flight path selection must satisfy: 1 ​​≥5 (7) (8) (9) (10) (11) The weight of the UAV itself is 0.5 is the energy required for the UAV to return in real time. 5=kVn 2 +γm0gZn (12) Similarly, the distance of the UAV in the X direction of the return flight is 2, the distance in the Y direction is 2, and the distance in the Z direction is 2. The flight time in the X direction, the flight time in the Y direction is Tb, and the flight time in the Z direction is. u is the correlation coefficient of the flight time in the X direction. m is the weight of the UAV when carrying the charging equipment.Flight time 3 is: T3 = Tb = The total energy consumption of the UAV's return flight is: 1 = 0 3 5 and the flight path selection must satisfy: 1 ​​≥ 1 min E.

[0018] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the concept and scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the inventive concept should fall within the protection scope of the present invention. All technical contents for which protection is sought in this invention are fully described in the claims.

Claims

1. A cloud-wing 6G airborne power transmission drone, characterized in that: The unmanned aerial vehicle body is connected to the charging device through electromagnetic suction, and a digital twin computing system is arranged on the unmanned aerial vehicle body, and the algorithm of the digital twin computing system is as follows: First, the unmanned aerial vehicle receives a task signal and obtains the specific position (1, 1, 1) of the task target, and the real-time position of the unmanned aerial vehicle during flight is represented as (x, y, z), the real-time flight speed of the unmanned aerial vehicle is 0.k is a related resistance coefficient related to the speed during flight; is the energy consumption of the unmanned aerial vehicle during flight; γ is a related coefficient of the energy consumption of the unmanned aerial vehicle during hovering; the specific position of the task target is input, and then H route simulations are calculated according to the actual environmental conditions, and the flight time is simulated; H=50 (1) 3The distance in the X direction of the unmanned aerial vehicle flight is x, the distance in the Y direction is y, and the distance in the Z direction is z; the flight time in the X direction is T x, the flight time in the Y direction is T y, and the flight time in the Z direction is T z; m is the weight of the unmanned aerial vehicle carrying the charging device; the flight time T is: T=T x +T y +T z =kVn 2 +γmgZn When hovering, =0The total energy consumption of the unmanned aerial vehicle flight is: E=0(2)(3)(4)(5)The optimal route is obtained by comparing the energy consumption of the H route simulations.