Unmanned aerial vehicle swarm multi-task target self-brushing adjustable distribution device and method

Abstract

The invention relates to the technical field of unmanned aerial vehicle swarms, and discloses an unmanned aerial vehicle swarm multi-task target self-brushing adjustable distribution device and method, and an unmanned aerial vehicle body, the unmanned aerial vehicle body is provided with a master controller, a camera and an adjusting mechanism, the master controller is connected with the camera and the adjusting mechanism, and the camera is connected with the adjusting mechanism. Through arrangement of a multi-task target self-brushing adjustable distribution mechanism and an adjusting mechanism, a main reference angle marker of one unmanned aerial vehicle acquires angle information of a corresponding electronic compass as a main reference angle, and other electronic compasses acquire angles of corresponding cameras; the set included angle between the cameras between every two adjacent unmanned aerial vehicle bodies is adjusted through the adjusting assembly, distribution of self-brushing adjustment of multi-task targets is completed, all the cameras inspect all the targets, the use convenience of the module is improved, and the inspection efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of drone swarms, and in particular to a drone swarm multi-task target self-brush adjustable distribution device. Background Art

[0002] The principles of drone swarm technology primarily encompass three aspects: communication, control, and collaboration. First, communication is the foundation of drone swarm technology. UAVs require real-time communication to exchange location, status, and mission information. Second, control is the core of drone swarm technology, requiring precise control of drones to ensure efficient mission completion. Finally, collaboration is key to drone swarm technology, requiring multiple drones to collaborate and cooperate to achieve optimal mission results.

[0003] When inspecting buildings such as bridges or roads, the drone swarm's photography is relatively single, unable to adjust itself according to multi-task objectives, and unable to adaptively control the camera's shooting angle, which requires repeated operations and is cumbersome to operate.

[0004] In order to solve the above problems, this application proposes a self-brush adjustable distribution device for multi-task targets of a drone swarm. Summary of the Invention

[0005] The purpose of the present invention is to provide a self-brush adjustable distribution device for multi-task targets of a drone swarm, so as to solve the problem in the prior art proposed in the above background technology that when inspecting buildings such as bridges or roads, the self-brush adjustment cannot be performed according to the multi-task targets, and the shooting angle of the camera cannot be adaptively controlled.

[0006] To achieve the above-mentioned object, the present invention provides the following technical solution: a self-brush adjustable distribution device for multi-task targets of a drone swarm, comprising a drone body, wherein the drone body is equipped with a master controller, a camera, and an adjustment mechanism, wherein the master controller is connected to the camera and the adjustment mechanism respectively, wherein:

[0007] The adjustment mechanism is used to adjust the camera angle;

[0008] The master controller is equipped with a multi-task target self-brush adjustable distribution mechanism for collecting the angles required by the camera during the inspection of the drone body. The multi-task target self-brush adjustable distribution mechanism includes an angle communicator, an electronic compass and a main reference angle marker. The electronic compass is used to collect the real-time angle of the drone. The master controller in the same drone body can obtain the information of the electronic compass in the drone body. The angle communicator is used to send and receive the information of the electronic compass sent by the master controller. The angle communicator can transmit the angle information to the master controllers in other drone bodies. The master controllers on other drone bodies receive the angle information through the corresponding angle communicators.

[0009] The main reference angle marker serves as the central machine during the inspection of the drone body and is used as the main reference for angle adjustment. Only one main reference angle marker is required to operate and obtain the angle information of its corresponding electronic compass as the main reference angle. The main controller obtains the main reference angle and sends it to other drone bodies through the angle communicator.

[0010] Furthermore, the angle communicator includes an angle signal transmitter and an angle signal receiver, wherein the angle signal transmitter is used to transmit the angle information of the electronic compass to the main controller in the other drone body, and the other main controller can receive the angle information through the angle signal receiver. When one of the main reference angle markers is running, the angle obtained by the corresponding electronic compass is used as the main reference angle and transmitted to the main controller serving as the source main controller. The controller sends the main reference angle to the other main controllers through the angle signal receiver, and the other main controllers receive the main reference angle through the angle signal receiver.

[0011] Furthermore, the main controller includes a flight control module, a flight module, a picture signal transmission module, a picture storage and a communication module. The flight control module cooperates with the flight module to control the direction of flight. The picture signal transmission module and the picture storage are used to transmit and store the pictures taken by the camera. The communication module is used for communication between each drone body and between the drone body and the ground station.

[0012] Furthermore, the master controller also includes an environmental monitoring module, a battery module, and a radar module.

[0013] Furthermore, the adjustment mechanism includes a connecting tube, a horizontal adjustment component and a vertical adjustment component, wherein the connecting tube is installed in the casing, extends downward through the casing and its bottom end is fixedly connected to the camera, the horizontal adjustment component is installed horizontally outward of the connecting tube, and the vertical adjustment component is also installed on the connecting tube.

[0014] Furthermore, the horizontal adjustment assembly includes a motor controller A, a motor A, a worm A, a worm wheel A, a casing and a connecting rod, wherein an installation groove A is opened on the inner side of the right end of the casing, the motor A is fixedly connected to the top of the inner wall of the installation groove A, the motor A is connected to the motor controller A, the output end of the motor A is fixedly connected to the worm A, the rear end face of the worm A is engaged with the worm wheel A, the inner side of the worm wheel A is fixedly connected to the connecting rod, the end of the connecting rod is rotatably connected to the casing through a rotating shaft, and the connecting rod is fixedly connected to the casing.

[0015] Furthermore, the vertical adjustment assembly includes a motor controller B, a motor B, a worm B, a worm wheel B and a rotating block. A mounting slot B is provided at the top of the casing. The right end of the inner wall of the mounting slot B is fixedly connected to the motor B. The motor B is connected to the motor controller B. The output end of the motor B is fixedly connected to the worm B. The left end of the worm B is rotatably connected to the left end of the inner wall of the mounting slot B through a rotating shaft. The rear end face of the worm B is engaged with a worm wheel B. The inner side of the worm wheel B is fixedly connected to the connecting pipe. A limiting slot is provided on the inner side of the casing. The inner side of the limiting slot is rotatably connected to the rotating block. The inner side of the rotating block is fixedly connected to the connecting pipe. The connecting pipe passes through the surface of the rotating block and the connecting pipe passes through the surface of the casing.

[0016] The present invention also discloses a self-brush adjustable allocation method for multi-task targets of a drone swarm, comprising:

[0017] Obtaining angle information: The main controller obtains electronic compass information to obtain angle information, wherein obtaining the main reference angle: the main reference angle marker of the center machine operates, and the main controller obtains electronic compass information to obtain the main reference angle;

[0018] Send angle information. The angle communicator obtains angle information from the main controller and sends it to other drone bodies.

[0019] Receive angle information. The main controller on other drone bodies receives angle information through the corresponding angle communicator;

[0020] Adjustment, the main controller of each drone controls the adjustment mechanism to adjust the camera angle according to the angle information.

[0021] Furthermore, during the operation of each drone, efficiency, risk, energy, uniformity and communication cost need to be comprehensively considered, and the weighted optimization function is: O = αE-βR+γS+δU-∈C, where E represents the time efficiency index, R represents the risk index, S represents the energy efficiency index, U represents the uniformity of the task, C represents the communication cost, α is the time efficiency weight coefficient, β is the risk weight coefficient, γ is the energy efficiency weight coefficient, δ is the uniformity weight coefficient, and ∈ is the communication cost weight coefficient.

[0022] Furthermore, the efficiency objective function is:

[0023]

[0024] Where n represents the number of tasks, i represents the i-th task, and t i represents the completion time of task i, p i represents the importance coefficient of task i, w ij represents the number of obstacles encountered by UAV j when performing mission i, c krepresents the time cost related to the environment during task execution, φ ij represents the execution efficiency coefficient of UAV j in task i, t ij represents the time when UAV j performs task i, m represents the number of UAVs, k represents the environmental factors, and l represents the environmental factors at the corresponding time.

[0025] Furthermore, the risk objective function is:

[0026]

[0027] Among them, r i represents the risk factor of task i, q j represents the risk factor of drone j, r ij represents the risk factor of drone j performing mission i, o k represents the risk coefficient of environmental factor k, v ik represents the degree to which task i is affected by environmental factor k, ψ ij represents the risk weight of drone j in mission i, and l represents the environmental factors at the corresponding moment.

[0028] Furthermore, the energy efficiency objective function is:

[0029]

[0030] Among them, b j represents the initial power of drone j, e ij represents the power consumption of drone j when performing task i, f ij represents the power consumption of drone j due to environmental factors when performing mission i, η ij represents the power efficiency weight of UAV j in task i, b ij represents the battery capacity of drone j in mission i.

[0031] Furthermore, the task uniformity function is:

[0032]

[0033] t ij represents the time for UAV j to perform task i, t i represents the standard completion time of task i, ξ ij represents the task uniformity weight, u ij represents the uniformity index of UAV j in mission i.

[0034] Furthermore, the communication cost is:

[0035]

[0036] Among them, dij represents the communication distance between UAV j and the control center when performing mission i, κ ij represents the communication distance weight, c ij represents the communication cost when UAV j performs task i, λ ij Represents the communication cost weight.

[0037] Furthermore, genetic algorithm, multi-objective particle swarm optimization or multi-objective evolutionary algorithm are used for the multi-objective optimization of risk objective function to obtain a series of optimal solutions, which constitute the Pareto frontier. By analyzing these solutions, the drone allocation strategy that best meets the current mission requirements and resource constraints is selected.

[0038] Furthermore, it includes adaptive adjustment and reallocation, specifically including: adaptive adjustment model construction; construction of task allocation optimization model; dynamic reallocation strategy.

[0039] Furthermore, the adaptive adjustment model construction includes:

[0040]

[0041] , A ij is the adaptive coefficient, which indicates the adaptability of UAV j to perform task i, where s j represents the current state of UAV j, t ij represents the time required to execute task i, eij represents the battery consumption rate of executing task i, d ij represents the communication distance to the control center, h ij represents the altitude of UAV j when performing mission i, k j represents the maneuverability of UAV j, g ij represents the complexity of UAV j performing task i, θ j represents the failure rate of UAV j; α1 is the weight coefficient of the current state of the UAV, α2 is the weight coefficient of the time required to perform the mission, α3 is the weight coefficient of the battery consumption rate, α4 is the weight coefficient of the communication distance with the control center, α5 is the weight coefficient of the mission execution altitude, α6 is the weight coefficient of maneuverability, α7 is the weight coefficient of complexity, and α8 is the weight coefficient of the failure rate.

[0042] Furthermore, the optimization model for task allocation is constructed including: Among them, p ij is the probability that task i is assigned to drone j, A ij New is the new adaptive coefficient after redistribution.

[0043] Furthermore, the dynamic reallocation strategy includes:

[0044] State change detection: If s j<σ, then redistribution is performed, where s j represents the key status indicator of UAV j, σ represents the preset threshold;

[0045] According to the key status indicators s of UAV j j Recalculate the new adaptive coefficient A ij New ;

[0046] Based on the new adaptive coefficients, an optimization algorithm is used to redistribute tasks to maximize the total adaptive coefficient of the swarm:

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The present invention sets up a multi-task target self-brush adjustable distribution mechanism and an adjustment mechanism. After taking off, the drones are divided into two groups on both sides of the bridge body. The two groups of drones are symmetrically arranged. The main reference angle marker of one drone obtains the angle information of its corresponding electronic compass as the main reference angle, and the other electronic compasses obtain the angles of their corresponding cameras. Combined with the set angles of the cameras between each adjacent drone body, the adjustment components are adjusted to complete the self-brush adjustment distribution of the multi-task targets, so that each camera can inspect each target, thereby improving the convenience of module use and improving inspection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a schematic diagram of the installation structure of the drone body of the present invention;

[0050] Figure 2 Schematic diagram of the installation structure of the master controller of the present invention;

[0051] Figure 3 Schematic diagram of the installation structure of the adjustment mechanism of the present invention;

[0052] Figure 4 This is a schematic diagram of the installation structure of the level adjustment assembly of the present invention;

[0053] Figure 5 It is a schematic diagram of the installation structure of the vertical adjustment assembly of the present invention;

[0054] In the figure: 1. UAV body; 11. Casing; 2. Main controller; 21. Flight control module; 22. Communication module; 23. Image signal transmission module; 24. Image memory; 3. Multi-task target self-brush adjustable distribution mechanism; 31. Angle communicator; 311. Angle signal transmitter; 312. Angle signal receiver; 32. Electronic compass; 33. Main reference angle marker; 4. Adjustment mechanism; 41. Connecting pipe; 42. Horizontal adjustment assembly; 421. Motor controller A; 422. Motor A; 423. Worm A; 424. Worm gear A; 425. Housing; 426. Connecting rod; 43. Vertical adjustment assembly; 431. Motor controller B; 432. Motor B; 433. Worm B; 434. Worm gear B; 435. Rotating block; 5. Camera. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0056] The standard parts used in this application can all be purchased from the market, and special-shaped parts can be customized according to the instructions and drawings. The specific connection methods of each part adopt conventional means such as mature bolts, rivets, welding, and bonding in the existing technology, and the components used for circuit connection are all conventional models in the existing technology.

[0057] At the same time, in order to clearly express the connection relationship and working principle between the various components and highlight the key points, the drawings in the specification are organized and drawn in the form of simple diagrams. One simple diagram can correspond to a variety of materials and actual external structural shapes.

[0058] See also Figure 1-Figure 5 The present invention provides a technical solution: a self-brush adjustable distribution device for multi-task targets of a drone swarm, comprising a drone body 1, which is equipped with a master controller 2, a camera 5, and an adjustment mechanism 4. The master controller 2 is fixedly installed inside the drone body 1, and the camera 5 is installed below the drone. In addition, the master controller 2 is connected to the camera 5 and the adjustment mechanism 4, respectively, wherein:

[0059] The adjustment mechanism 4 is used to adjust the angle of the camera 5;

[0060] The main controller 2 is equipped with a multi-task target self-brush adjustable distribution mechanism 3 for collecting the angles required by the camera 5 during the inspection of the drone body 1. The multi-task target self-brush adjustable distribution mechanism 3 includes an angle communicator 31, an electronic compass 32 and a main reference angle marker 33. The electronic compass 32 can be fixedly connected to the outside of the top of the camera 5. The main controller 2 is connected to the electronic compass 32 and the main reference angle marker 33. The electronic compass 32 is used to collect the real-time angle of the drone. The main controller 2 in the same drone body 1 can obtain the information of the electronic compass 32 in the drone body 1. The angle communicator 31 is used to send and receive information from the electronic compass 32 sent by the main controller 2. The angle communicator 31 can transmit the angle information to the main controllers 2 in other drone bodies 1. The main controllers 2 on other drone bodies 1 receive the angle information through the corresponding angle communicator 31.

[0061] The main reference angle marker 33 serves as the central machine during the inspection of the drone body 1 and is used as the main reference for angle adjustment. Only one main reference angle marker 33 is required to operate to obtain the angle information of its corresponding electronic compass 32 as the main reference angle. The main controller 2 obtains the main reference angle and sends it to other drone bodies 1 through the angle communicator 31.

[0062] In this embodiment, there are two groups of drone bodies 1, and the two groups of drone bodies 1 are symmetrically arranged on both sides. When the drone bodies 1 are swarming to inspect the bridge, the two groups of drone bodies 1 are symmetrically placed on both sides of the bridge for inspection.

[0063] When the present invention is working, the drones 1 are divided into two groups after taking off and are respectively on both sides of the bridge body. The two groups of drones 1 are symmetrically arranged. The main reference angle marker 33 of one drone 1 obtains the angle information of its corresponding electronic compass 32 as the main reference angle, and the other electronic compasses 32 obtain the angle of its corresponding camera 5. The angle of the camera 5 between each adjacent drone body 1 is adjusted by the adjustment component 4 to complete the self-brush adjustment distribution of multi-task targets, so that each camera 5 can inspect each target. Specifically, the main controller 2 obtains the electronic compass 32 information and obtains the angle information, wherein the main reference angle marker 33 of the central machine is running, and the main controller 2 obtains the electronic compass 32 information through the main reference angle marker 33 to obtain the main reference angle; the angle communicator 31 obtains the angle information including the main reference angle from the main controller 2 and sends it to the other drone bodies 1; the main controllers 2 on the other drone bodies 1 receive the angle information through the corresponding angle communicator 31; the main controller 2 of each drone controls the adjustment mechanism 4 to adjust the camera 5 angle according to the angle information.

[0064] The adjustment mechanism 4 adjusts the angle of the camera 5. After the main controller 2 on the other drone body 1 receives the main reference angle information of the main reference angle marker 33 through the angle signal receiver 312, the angle adjustment signal is transmitted to the motor controller A421 and / or the motor controller B431. The motor controller A421 controls the operation of the motor A422, and the motor controller B431 controls the operation of the motor B432, thereby adjusting the angle of each camera 5. After the angle of each camera 5 is adjusted, the corresponding electronic compass 32 continues to obtain the angle and transmits it to the main controller 2 of the same drone body 1, and performs self-checking against the reference angle of the main reference angle marker 33.

[0065] like Figure 2 The angle communicator 31 includes an angle signal transmitter 311 and an angle signal receiver 312, wherein the angle signal transmitter 311 is used to transmit the angle information of the electronic compass 32 to the main controller 2 in the other drone body 1, and the other main controller 2 can receive the angle information through the angle signal receiver 312. When one of the main reference angle markers 33 is running, the angle obtained by the corresponding electronic compass 32 is used as the main reference angle and transmitted to the main controller 2 as the source main controller. The controller 2 sends the main reference angle to the other main controllers 2 through the angle signal receiver 312, and the other main controllers 2 receive the main reference angle through the angle signal receiver 312.

[0066] The main controller 2 includes a flight control module 21, a flight module, a picture signal transmission module 23, a picture memory 24, a communication module 22, an environmental monitoring module, a battery module, and a radar module. The flight control module 21 cooperates with the flight module to control the direction of flight. The picture signal transmission module 23 and the picture memory 24 are used to transmit and store the pictures taken by the camera 5. The communication module 22 is used for communication between each drone body 1 and between the drone body 1 and the ground station.

[0067] Figure 3As shown, the adjustment mechanism 4 includes a connecting tube 41, a horizontal adjustment assembly 42, and a vertical adjustment assembly 43. The connecting tube 41 is installed within the housing 11, extending downward through the housing 11 and having its bottom end fixedly connected to the camera 5. The horizontal adjustment assembly 42 is installed horizontally outward from the connecting tube 41, and the vertical adjustment assembly 43 is also installed on the connecting tube 41. The drone body 1 transmits the angle adjustment signal to other drone bodies 1 via the communication module 22 based on the angle set for the camera 5 between each adjacent drone body 1, based on the angle information collected by the multi-task target self-brush adjustable distribution mechanism 3. After receiving the angle task adjustment signal, the communication module 22 on the other drone body 1 transmits it to the horizontal adjustment assembly 42 or the vertical adjustment assembly 43. The horizontal adjustment assembly 42 controls the horizontal adjustment of the camera 5, while the vertical adjustment assembly 43 controls the vertical adjustment of the camera 5.

[0068] Figure 4 As shown, the horizontal adjustment component 42 includes a motor controller A421, a motor A422, a worm A423, a worm wheel A424, a sleeve 425 and a connecting rod 426, wherein an installation slot A is opened on the inner side of the right end of the casing 11, and the top of the inner wall of the installation slot A is fixedly connected to the motor A422, the motor A422 is connected to the motor controller A421, the output end of the motor A422 is fixedly connected to the worm A423, the rear end face of the worm A423 is engaged with the worm wheel A424, and the inner side of the worm wheel A424 is fixedly connected to the connecting rod 426, the end of the connecting rod 426 is rotatably connected to the casing 11 through a rotating shaft, and the connecting rod 426 is fixedly connected to the sleeve 425. The motor controller A421 receives the difference between the main reference angle and the set horizontal angle of the camera 5 between each adjacent drone body 1, and the motor controller A421 controls the motor A422 to rotate forward or reverse. The operation of the motor A422 drives the worm A423 to rotate, and the worm A423 drives the sleeve 425 to rotate through the worm gear A424. The sleeve 425 drives the connecting tube 41 to rotate and then drives the camera 5 to rotate, so that the camera 5 adjusts the horizontal angle.

[0069] Figure 5As shown, the vertical adjustment component 43 includes a motor controller B431, a motor B432, a worm B433, a worm gear B434 and a rotating block 435. A mounting slot B is provided at the top of the sleeve 425. The right end of the inner wall of the mounting slot B is fixedly connected to the motor B432. The motor B432 is connected to the motor controller B431. The output end of the motor B432 is fixedly connected to the worm B433. The left end of the worm B433 is rotatably connected to the left end of the inner wall of the mounting slot B through a rotating shaft. The rear end face of the worm B433 is engaged with the worm gear B434. The inner side of the worm gear B434 is fixedly connected to the connecting pipe 41. A limiting slot is provided on the inner side of the sleeve 425. The rotating block 435 is rotatably connected to the inner side of the limiting slot. The inner side of the rotating block 435 is fixedly connected to the connecting pipe 41. The connecting pipe 41 passes through the surface of the rotating block 435. The connecting pipe 41 passes through the surface of the sleeve 425. The motor controller B431 receives the difference between the main reference angle and the vertical angle that the camera 5 should have between each adjacent drone body 1. The motor controller A421 controls the motor A422 to rotate forward or reverse. The operation of the motor A422 drives the worm A423 to rotate. The worm A423 drives the sleeve 425 to rotate through the worm gear A424. The sleeve 425 drives the connecting tube 41 to rotate and then drives the camera 5 to rotate. The camera 5 adjusts the horizontal angle and completes the self-brush adjustment distribution of multi-task targets, so that each camera 5 can inspect each target, improve the convenience of module use, and improve inspection efficiency.

[0070] The self-brush adjustable allocation method for multi-task targets of UAV swarm includes:

[0071] Obtaining angle information: the main controller 2 obtains information from the electronic compass 32 to obtain angle information, wherein the main reference angle is obtained: the main reference angle marker 33 of the center machine is running, the main controller 2 obtains information from the electronic compass 32 to obtain the main reference angle;

[0072] Sending angle information: the angle communicator 31 obtains the angle information from the main controller 2 and sends it to other drone bodies 1;

[0073] Receiving angle information, the main controller 2 on the other drone body 1 receives the angle information through the corresponding angle communicator 31;

[0074] Adjustment: The main controller 2 of each drone controls the adjustment mechanism 4 to adjust the angle of the camera 5 according to the angle information.

[0075] Multi-objective optimization: During the operation of each drone, efficiency, risk, energy, uniformity and communication cost need to be comprehensively considered. The weighted optimization function is: O = αE-βR+γS+δU-∈C, where E represents the time efficiency index, R represents the risk index, S represents the energy efficiency index, U represents the uniformity of the task, C represents the communication cost, α is the time efficiency weight coefficient, β is the risk weight coefficient, γ is the energy efficiency weight coefficient, δ is the uniformity weight coefficient, and ∈ is the communication cost weight coefficient.

[0076] Efficiency objective function E:

[0077]

[0078] Efficiency depends not only on time but also on the importance of the task. i represents the completion time of task i, p i represents the importance coefficient of task i (more important tasks have higher coefficients), w ij represents the number of obstacles encountered by UAV j when performing mission i, c k represents the time cost related to the environment during task execution (such as the impact of wind speed), φ ij represents the execution efficiency coefficient of UAV j in task i, t ij represents the time when UAV j performs task i, m represents the number of UAVs, k represents the environmental factors, and l represents the environmental factors at the corresponding time.

[0079] Risk objective function R:

[0080] Risk can be calculated based on various factors, such as the probability of damage to the drone, the dangerousness of the flight conditions, etc. If the risk of each task i is r i And the risk coefficient of each drone j is q j , then the risk objective function can be expressed as:

[0081]

[0082] Among them, r i represents the risk factor of task i, q j represents the risk factor of drone j, r ij represents the risk factor of drone j performing mission i, o k represents the risk coefficient of environmental factor k, v ik represents the degree to which task i is affected by environmental factor k, ψ ij represents the risk weight of drone j in mission i, and l represents the environmental factors at the corresponding moment.

[0083] Energy efficiency objective function S:

[0084] Energy efficiency is usually proportional to the remaining power of the drone. If the initial power of drone j is b j And the power consumed by executing task i is e ij , then the energy efficiency objective function can be expressed as:

[0085]

[0086] Maximize the total remaining power of all drones to improve energy efficiency. j represents the initial power of drone j, e ij represents the power consumption of drone j when performing task i, f ij represents the increased power consumption of UAV j due to environmental factors (such as wind speed) when performing mission i, η ij represents the power efficiency weight of UAV j in task i, b ij represents the battery capacity of drone j in mission i.

[0087] Task uniformity function U:

[0088]

[0089] t ij represents the time for UAV j to perform task i, t i represents the standard completion time of task i, ξ ij represents the task uniformity weight, u ij represents the uniformity index of UAV j in mission i.

[0090] Communication cost C:

[0091]

[0092] Among them, d ij represents the communication distance between UAV j and the control center when performing mission i, κ ij represents the communication distance weight, c ij represents the communication cost when UAV j performs task i, λ ij Represents the communication cost weight.

[0093] Choosing a multi-objective optimization algorithm: Solving multi-objective optimization problems typically requires specialized algorithms, such as genetic algorithms, multi-objective particle swarm optimization (MOPSO), or multi-objective evolutionary algorithms (MOEA). These algorithms can find effective trade-offs between multiple objectives and generate a series of optimal solutions, forming the Pareto front. By analyzing these solutions, the UAV allocation strategy that best meets the current mission requirements and resource constraints can be selected.

[0094] Adaptive adjustment and reallocation of drones during mission execution, including:

[0095] 1. Adaptive adjustment model construction:

[0096] Adaptive adjustment requires that the algorithm can adjust according to the current state of the drone and the changes in the mission environment. This is achieved by building an adaptive adjustment model. Set an adaptive coefficient A ij , represents the adaptability of UAV j to perform task i: A ij =f(s j , t ij , e ij , d ij , h ij , k j , g ij ,θ j ), where s j represents the current state of UAV j, t ij represents the time required to execute task i, eij represents the battery consumption rate of executing task i, d ij represents the communication distance to the control center, h ij represents the altitude of UAV j when performing mission i, k j represents the maneuverability of UAV j, g ij represents the complexity of UAV j performing task i, θ j represents the failure rate of UAV j.

[0097] 2. Specific calculation of adaptive coefficient:

[0098] The calculation of the adaptive coefficient involves a comprehensive evaluation of multiple factors. These factors are linearly combined:

[0099]

[0100] Among them, α1, α2, α3, α4, α5, α6, α7, and α8 are weight coefficients used to balance the importance of different factors. Specifically, α1 is the weight coefficient of the current state of the drone, α2 is the weight coefficient of the time required to execute the mission, α3 is the weight coefficient of the battery consumption rate, α4 is the weight coefficient of the communication distance to the control center, α5 is the weight coefficient of the mission altitude, α6 is the weight coefficient of maneuverability, α7 is the weight coefficient of complexity, and α8 is the weight coefficient of the failure rate.

[0101] 3. Optimization model of task allocation:

[0102] Based on the adaptive coefficients, an optimization model can be constructed to redistribute tasks: Among them, p ijis the probability that task i is assigned to drone j. This maximizes the sum of the adaptive coefficients of the entire swarm, thereby optimizing task allocation. ij New is the new adaptive coefficient after redistribution.

[0103] 4. Dynamic reallocation strategy

[0104] The core of the dynamic reallocation strategy is to respond to changes in the status of drones and adjust task allocation in a timely manner to maintain the efficiency and safety of the entire system. The following is a formal description and method of this strategy:

[0105] a. State change detection:

[0106] For each drone j, monitor its key status indicators s j (For example, power consumption, hardware health index, etc.) When these indicators fall below a preset threshold σ, the reallocation process is triggered:

[0107] If s j <σ, then redistribution is performed. Here, the threshold σ can be set according to the specific type of UAV and the nature of the mission to be performed.

[0108] b. Recalculate the adaptive coefficient:

[0109] After the state change triggers the reallocation, the adaptive coefficient A of the affected drones is recalculated. ij This may involve updating its corresponding task time t ij Energy consumption ij and communication distance d ij Assessment:

[0110]

[0111] c. Optimize task allocation:

[0112] Based on the new adaptive coefficients, an optimization algorithm (such as linear programming, genetic algorithm, etc.) is used to redistribute tasks to maximize the total adaptive coefficient of the swarm: The optimization algorithm here should take into account factors such as the urgency of the mission and the remaining mission capabilities of the drone.

[0113] 5. Algorithm Implementation

[0114] The key to implementing the algorithm lies in efficiently processing real-time data and making quick decisions. The following is the methodology for implementing the algorithm:

[0115] Real-time data processing: Implement a data processing module to collect and analyze real-time status data from the drone. This includes, but is not limited to, battery level, location, flight speed, and mission progress. The data processing module should be able to quickly identify key status changes and trigger the reallocation process.

[0116] Decision-making algorithm design: Design a decision-making algorithm to reallocate tasks when the state changes. This algorithm should consider the UAV's current capabilities, mission requirements, and priorities. The decision-making algorithm needs to be flexible and adaptable to cope with various unexpected situations.

[0117] Learning and Optimization Mechanisms: Introducing machine learning mechanisms, such as reinforcement learning-based methods, allows algorithms to learn from historical data and optimize their decision-making strategies. Learning mechanisms can help algorithms better understand the impact of task allocation and make more effective adjustments in future decisions.

[0118] Dynamic adjustment and feedback mechanism: Implement a feedback mechanism to evaluate the execution effect of tasks after reallocation and adjust algorithm parameters based on the feedback results.

[0119] This dynamic adjustment enables the algorithm to more precisely adapt to the actual operating environment and mission requirements. The key to achieving this is real-time monitoring of the drone's status and mission environment, and dynamically adjusting task allocation based on this information. This requires the algorithm to possess efficient data processing capabilities and rapid decision-making capabilities. Furthermore, the algorithm must possess learning capabilities, adjusting the weight coefficients α1 for the drone's current state, α2 for the time required to execute the mission, α3 for the battery consumption rate, α4 for the communication distance to the control center, α5 for the mission altitude, α6 for maneuverability, α7 for complexity, and α8 for the failure rate based on historical data. By implementing these methods, the dynamic reallocation strategy and algorithm for the drone swarm will be able to efficiently respond to various state changes and mission requirements, ensuring the stability of the entire swarm and the efficiency of mission execution.

[0120] After takeoff, the drones 1 of the present invention are divided into two groups on both sides of the bridge body. The two groups of drones 1 are symmetrically arranged. The main reference angle marker 33 of one drone 1 obtains the angle information of its corresponding electronic compass 32 as the main reference angle, and the other electronic compass 32 obtains the angle of its corresponding camera 5. The angle of the camera 5 between each adjacent drone body 1 is adjusted by the adjustment component 4 to complete the self-brush adjustment distribution of multi-task targets, so that each camera 5 can inspect each target, improve the convenience of module use, and improve inspection efficiency.

Claims

1. A self-brush adjustable distribution device for multi-task targets of drone swarms, characterized in that: The invention comprises an unmanned aerial vehicle (UAV) body (1), wherein the UAV body (1) is equipped with a master controller (2), a camera (5) and an adjustment mechanism (4), wherein the master controller (2) is connected to the camera (5) and the adjustment mechanism (4), respectively, wherein: The adjustment mechanism (4) is used to adjust the angle of the camera (5); The master controller (2) is provided with a multi-task target self-brush adjustable distribution mechanism (3) for collecting the angle required by the camera (5) during the inspection of the drone body (1). The multi-task target self-brush adjustable distribution mechanism (3) includes an angle communicator (31), an electronic compass (32) and a main reference angle marker (33). The electronic compass (32) is used to collect the real-time angle of the drone. The master controller (2) in the same drone body (1) can obtain the information of the electronic compass (32) in the drone body (1). The angle communicator (31) is used to send and receive the information of the electronic compass (32) sent by the master controller (2). The angle communicator (31) can transmit the angle information to the master controller (2) in other drone bodies (1). The master controller (2) on the other drone body (1) receives the angle information through the corresponding angle communicator (31). The main reference angle marker (33) serves as a central machine for the drone body (1) during inspection and is used as a main reference for angle adjustment. Only one main reference angle marker (33) is required to operate, and the angle information of the corresponding electronic compass (32) is obtained as the main reference angle. The main controller (2) obtains the main reference angle and sends it to other drone bodies (1) through the angle communicator (31).

2. The UAV swarm multi-task target self-brush adjustable distribution device according to claim 1 is characterized in that: The angle communicator (31) comprises an angle signal transmitter (311) and an angle signal receiver (312), wherein the angle signal transmitter (311) is used to transmit angle information of the electronic compass (32) to a master controller (2) in another unmanned aerial vehicle body (1), and the other master controller (2) can receive the angle information through the angle signal receiver (312). When one of the main reference angle markers (33) is in operation, the angle obtained by the corresponding electronic compass (32) is used as the main reference angle and transmitted to the master controller (2) serving as the source master controller. The controller (2) transmits the main reference angle to the other master controller (2) through the angle signal receiver (312), and the other master controller (2) receives the main reference angle through the angle signal receiver (312).

3. The UAV swarm multi-task target self-brush adjustable distribution device according to claim 2, characterized in that: The adjustment mechanism (4) comprises a connecting pipe (41), a horizontal adjustment component (42) and a vertical adjustment component (43), wherein the connecting pipe (41) is installed in the housing (11), the connecting pipe (41) extends downward through the housing (11) and the bottom end thereof is fixedly connected to the camera (5), the horizontal adjustment component (42) is installed horizontally outward of the connecting pipe (41), and the vertical adjustment component (43) is also installed on the connecting pipe (41).

4. The UAV swarm multi-task target self-brush adjustable distribution device according to claim 3 is characterized in that: The horizontal adjustment assembly (42) includes a motor controller A (421), a motor A (422), a worm A (423), a worm wheel A (424), a casing (425) and a connecting rod (426), wherein a mounting slot A is provided on the inner side of the right end of the casing (11), the motor A (422) is fixedly connected to the top of the inner wall of the mounting slot A, the motor A (422) is connected to the motor controller A (421), the output end of the motor A (422) is fixedly connected to the worm A (423), the rear end face of the worm A (423) is engaged with the worm wheel A (424), the inner side of the worm wheel A (424) is fixedly connected to the connecting rod (426), the end of the connecting rod (426) is rotatably connected to the casing (11) through a rotating shaft, and the connecting rod (426) is fixedly connected to the casing (425); The vertical adjustment assembly (43) includes a motor controller B (431), a motor B (432), a worm B (433), a worm wheel B (434) and a rotating block (435). The top of the housing (425) is provided with a mounting slot B. The right end of the inner side wall of the mounting slot B is fixedly connected to the motor B (432). The motor B (432) is connected to the motor controller B (431). The output end of the motor B (432) is fixedly connected to the worm B (433). The left end of the worm B (433) is connected to the mounting slot B through a rotating shaft. The left end of the inner wall of the groove B is rotatably connected, the rear end face of the worm gear B (433) is meshed with a worm wheel B (434), the inner side of the worm wheel B (434) is fixedly connected to the connecting pipe (41), a limiting groove is provided on the inner side of the sleeve (425), the inner side of the limiting groove is rotatably connected to the rotating block (435), the inner side of the rotating block (435) is fixedly connected to the connecting pipe (41), the connecting pipe (41) passes through the surface of the rotating block (435), and the connecting pipe (41) passes through the surface of the sleeve (425).

5. A method for self-adjustable allocation of multi-task targets of drone swarms based on the device of claim 1, characterized in that: include: Acquiring angle information, the main controller (2) acquires information from the electronic compass (32) to obtain angle information, wherein the main reference angle is acquired: the main reference angle marker (33) of the center machine is operated, the main controller (2) acquires information from the electronic compass (32) to obtain the main reference angle; Sending angle information: the angle communicator (31) obtains the angle information from the main controller (2) and sends it to other drone bodies (1); Receiving angle information, the main controller (2) on the other drone body (1) receives the angle information through the corresponding angle communicator (31); Adjustment: The main controller (2) of each drone controls the adjustment mechanism (4) to adjust the angle of the camera (5) according to the angle information.

6. A method for self-adjustable allocation of multi-task targets of a drone swarm based on the device of claim 1 according to claim 5, characterized in that: During the operation of each drone, efficiency, risk, energy, uniformity and communication cost need to be comprehensively considered. The weighted optimization function is: O = αE-βR+γS+δU-∈C, where E represents time efficiency, R represents risk, S represents energy efficiency, U represents task uniformity, C represents communication cost, α is the time efficiency weight coefficient, β is the risk weight coefficient, γ is the energy efficiency weight coefficient, δ is the uniformity weight coefficient, and ∈ is the communication cost weight coefficient; The efficiency E is: Where n represents the number of tasks, i represents the i-th task, and t i represents the completion time of task i, p i represents the importance coefficient of task i, w ij represents the number of obstacles encountered by UAV j when performing mission i, c k represents the time cost related to the environment during task execution, φ ij represents the execution efficiency coefficient of UAV j in task i, t ij represents the time when drone j performs task i, m represents the number of drones, k represents the environmental factors, and l represents the environmental factors at the corresponding moment; The risk R is: Among them, r i represents the risk factor of task i, q j represents the risk factor of drone j, r ij represents the risk factor of drone j performing mission i, o k represents the risk coefficient of environmental factor k, v ik represents the degree to which task i is affected by environmental factor k, ψ ij represents the risk weight of drone j in mission i, and l represents the environmental factors at the corresponding moment; Energy efficiency S is: Among them, b j represents the initial power of drone j, e ij represents the power consumption of drone j when performing task i, f ij represents the power consumption of drone j due to environmental factors when performing mission i, η ij represents the power efficiency weight of UAV j in task i, b ij represents the battery level of drone j in mission i; Task uniformity U is: t ij represents the time for UAV j to perform task i, t i represents the standard completion time of task i, ξ ij represents the task uniformity weight, u ij represents the uniformity index of UAV j in mission i; The communication cost C is: Among them, d ij represents the communication distance between UAV j and the control center when performing mission i, κ ij represents the communication distance weight, c ij represents the communication cost when UAV j performs task i, λ ij represents the communication cost weight; Genetic algorithm, multi-objective particle swarm optimization or multi-objective evolutionary algorithm are used to optimize the risk objective function to obtain a series of optimal solutions, which constitute the Pareto frontier. By analyzing these solutions, the drone allocation strategy that best meets the current mission requirements and resource constraints is selected.

7. A method for self-adjustable allocation of multi-task targets of a drone swarm based on the device of claim 1 according to claim 6, characterized in that: It includes adaptive adjustment and reallocation, specifically including: adaptive adjustment model construction; construction of task allocation optimization model; dynamic reallocation strategy.

8. The method for self-adjustable allocation of multi-task targets of a drone swarm based on the device of claim 1 according to claim 7, characterized in that: Adaptive adjustment model building includes: ， A ij is the adaptive coefficient, which indicates the adaptability of UAV j to perform task i, where s j represents the current state of UAV j, t ij represents the time required to execute task i, eij represents the battery consumption rate of executing task i, d ij represents the communication distance to the control center, h ij represents the altitude of UAV j when performing mission i, k j represents the maneuverability of UAV j, g ij represents the complexity of UAV j performing task i, θ j represents the failure rate of UAV j; α1 is the weight coefficient of the current state of the UAV, α2 is the weight coefficient of the time required to perform the mission, α3 is the weight coefficient of the battery consumption rate, α4 is the weight coefficient of the communication distance with the control center, α5 is the weight coefficient of the mission execution altitude, α6 is the weight coefficient of maneuverability, α7 is the weight coefficient of complexity, and α8 is the weight coefficient of the failure rate.

9. The method for self-adjustable allocation of multi-task targets of a drone swarm based on the device of claim 1 according to claim 8, characterized in that: Building an optimization model for task allocation includes: Among them, p ij is the probability that task i is assigned to drone j, A ij New is the new adaptive coefficient after redistribution.

10. A method for self-adjustable allocation of multi-task targets of a drone swarm based on the device of claim 1 according to claim 9, characterized in that: Dynamic reallocation strategies include: State change detection: If s j <σ, then redistribution is performed, where s j represents the key status indicator of UAV j, σ represents the preset threshold; According to the key status indicators s of UAV j j Recalculate the new adaptive coefficient A ij New ; Based on the new adaptive coefficients, an optimization algorithm is used to redistribute tasks to maximize the total adaptive coefficient of the swarm:

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