Low-altitude unmanned aerial vehicle formation operation-oriented closed-loop system resource arrangement method and system and medium

Abstract

The application provides a closed-loop system resource arrangement method and system for low-altitude unmanned aerial vehicle formation operation and a medium. The method comprises the following steps: converting a power allocation optimization problem of multiple operation unmanned aerial vehicles performing a same control task in a downlink of a closed-loop system into a convex optimization problem of control instruction information quantity of a command center of the closed-loop system finally affecting operation tasks; determining constraint conditions of the convex optimization problem according to maximum transmission power of the command center and downlink channel conditions and processing capacity of each operation unmanned aerial vehicle in cooperative operation; and under the constraint conditions, iteratively solving the convex optimization problem to determine optimal transmission power allocation of the multiple operation unmanned aerial vehicles performing the same control task. The application maximizes overall control performance of cooperative tasks by optimizing power resource allocation, thereby improving system resource utilization efficiency and task reliability.

Description

Technical Field

[0001] This application relates to the field of resource orchestration technology in low-altitude information and communication networks, and in particular to a closed-loop system resource orchestration method, system and medium for low-altitude UAV formation operations. Background Technology

[0002] Beyond low-altitude logistics and inspection, low-altitude unmanned operations are booming. Operational drones play a crucial role in low-altitude scenarios such as forest fire fighting, emergency rescue, countering unauthorized flights, and online maintenance, forming an organic network to effectively support unmanned operational tasks. In complex and dynamic scenarios, the capabilities of a single drone are limited; therefore, task division and behavioral coordination among multiple drones are necessary to collaboratively complete an overall task, such as coordinated search and rescue or area coverage detection. However, in actual deployments, communication resources are often limited, and the channel conditions and operational capabilities of each operational drone vary significantly. How to rationally allocate limited resources to multiple drones on demand to ensure more efficient task completion is a core engineering challenge currently facing the field. Summary of the Invention

[0003] The purpose of this application is to provide a closed-loop system resource orchestration method, system, and medium for low-altitude UAV formation operations, which can solve at least one of the technical problems mentioned in the prior art.

[0004] One aspect of this application provides a closed-loop system resource orchestration method for low-altitude unmanned aerial vehicle (UAV) swarm operations. The method includes: transforming the power allocation optimization problem of multiple operational UAVs performing the same control task in the downlink of the closed-loop system into a convex optimization problem of the control command information quantity that ultimately affects the operational task from the command center of the closed-loop system; determining the constraints of the convex optimization problem based on the maximum transmission power of the command center and the downlink channel conditions and processing capabilities of each operational UAV in the collaborative operation; and iteratively solving the convex optimization problem under the constraints to determine the optimal transmission power allocation for multiple operational UAVs performing the same control task.

[0005] Furthermore, the convex optimization problem of the control command information quantity uses the transmission power of the control command transmitted from the command center to the operational UAV and the signal-to-interference-plus-noise ratio (SIR) coupling coefficient as optimization variables. The method further includes: decomposing the convex optimization problem of the control command information quantity into a first convex optimization subproblem and a second convex optimization subproblem of the control command information quantity. The first convex optimization subproblem of the control command information quantity uses the transmission power as the optimization variable and the maximization of the control command information quantity as the objective function. The second convex optimization subproblem of the control command information quantity uses the SIR coupling coefficient as the optimization variable and the minimization of the control command information quantity as the objective function. The iterative solution of the convex optimization problem to determine the optimal transmission power allocation for multiple operational UAVs to perform the same control task includes: determining the optimal transmission power allocation by alternately iteratively solving the first convex optimization subproblem and the second convex optimization subproblem of the control command information quantity.

[0006] Furthermore, the method includes: modeling the process of multiple operational drones performing the same control task as a discrete linear time-invariant system; and based on the state transition matrix of the discrete linear time-invariant system... The intrinsic entropy rate of the discrete linear time-invariant system is obtained, wherein the constraints of the convex optimization problem include: , In the formula, R represents the amount of control command information that ultimately affects the operation task from the command center; Let be the intrinsic entropy rate of the discrete linear time-invariant system.

[0007] Furthermore, the method also includes: based on the channel capacity between the k-th operational UAV and the command center. The amount of control command information that affects the operation task by the k-th UAV is determined by the available transmission time T of the control commands in the closed-loop system. The upper bound; based on the amount of control command information that affects the task by the k-th operating drone. To determine the amount of control instruction information R that ultimately affects the operational task from the command center.

[0008] Furthermore, the channel capacity between the k-th operational drone and the command center... The expression is: , in, Let be the signal-to-interference-plus-noise ratio (SIR) and coupling coefficient of the k-th channel. ; The sub-channel bandwidth for transmitting control commands from the command center to the k-th operational UAV; The transmission power for the command center to transmit control commands to the k-th operational UAV; For communication noise variance; The large-scale channel gain is expressed as follows: In the formula Indicates the first The distance between the operational drones and the command center This is the path loss index. This represents the shadow fading effect; K is the number of operational drones.

[0009] Furthermore, the method also includes: setting the channels of each operating UAV to be mutually orthogonal, wherein the constraints of the convex optimization problem further include: , In the formula, K represents the number of operational drones.

[0010] Furthermore, the constraints of the convex optimization problem also include: , , , , , in, This represents the maximum transmission power of the command center. This represents the maximum amount of data that the k-th operational drone can receive and process within one cycle.

[0011] Furthermore, determining the optimal transmission power allocation by alternately iteratively solving the first convex optimization subproblem and the second convex optimization subproblem of the control command information includes: Set convergence threshold Initialize the number of iterations , , ; Fixed signal-to-interference-plus-noise ratio (SINR) combined coupling coefficient Solve the first convex optimization subproblem to obtain the updated transmission power of the operational UAV for transmitting control commands. ; Fixed update of the transmission power of the operation drone for transmitting control commands Solve the second convex optimization subproblem to obtain the updated signal-to-interference-plus-noise ratio (SIR) combined coupling coefficient. ; Calculate the norm of the variable update. ,like If the algorithm converges, then the optimal transmission power allocation is recorded. The amount of control command information that ultimately affects the operational tasks from the command center. If so, proceed to the next step; otherwise, let , , Return to fixed The steps to solve the first convex optimization subproblem; Output optimal transmission power allocation and the amount of control command information from the command center that ultimately affects the operational tasks. .

[0012] Furthermore, the fixed signal-to-interference-plus-noise ratio (SINR) combined coupling coefficient The closed-form solution to the first convex optimization subproblem is: , in, It is the only water level parameter that satisfies the maximum transmission power constraint.

[0013] Furthermore, the method also includes: based on the amount of control command information that ultimately affects the operational task from the command center. With respect to the intrinsic entropy rate of the discrete linear time-invariant system The magnitude of the value determines the stability of the closed-loop system; if If the closed-loop system is stable, it can operate normally; if If this happens, the closed-loop system will be unstable and cannot operate normally.

[0014] Another aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the closed-loop system resource orchestration method for low-altitude UAV formation operations described above.

[0015] Another aspect of this application provides a closed-loop system resource orchestration system for low-altitude UAV formation operations, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the closed-loop system resource orchestration method for low-altitude UAV formation operations described above.

[0016] The closed-loop system resource orchestration method, system, and medium for low-altitude UAV formation operations in one or more embodiments of this application comprehensively consider the downlink channel conditions and differentiated processing capabilities of each UAV in collaborative operations. Under the premise of strictly meeting the working capacity limitations of each UAV, the overall control performance of the collaborative task is maximized by optimizing power resource allocation, thereby improving system resource utilization efficiency and task reliability.

[0017] Furthermore, the closed-loop system resource orchestration method, system, and medium for low-altitude UAV swarm operations in one or more embodiments of this application can decompose the complex problem of power allocation optimization for multiple UAVs performing the same control task into two convex optimization sub-problems. Through an alternating iterative optimization algorithm, the closed-loop system control performance can be maximized under limited resources. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of a low-altitude drone swarm performing a low-altitude operation.

[0019] Figure 2 This is a flowchart illustrating a closed-loop system resource orchestration method for low-altitude UAV formation operations according to an embodiment of this application.

[0020] Figure 3 This is a performance comparison chart of the proposed solution with traditional water injection solutions and power sharing solutions.

[0021] Figure 4 This is a diagram showing the number of iterations for a closed-loop system resource orchestration method for low-altitude UAV formation operations according to an embodiment of this application.

[0022] Figure 5 This is a schematic structural block diagram of a closed-loop system resource orchestration system for low-altitude UAV formation operations according to an embodiment of this application. Detailed Implementation

[0023] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses consistent with some aspects of this application as detailed in the appended claims.

[0024] Figure 1 This illustration depicts a scenario where a formation of low-altitude drones performs low-altitude operational tasks. (For example...) Figure 1 As shown, the sensing drone monitors the target in real time and uploads the collected sensing data to the command center. The command center processes and analyzes the sensing data, generates corresponding control commands, and then transmits the control commands to the drone via the communication link to drive it to complete the relevant tasks. The entire process forms a closed loop of "sensing-communication-computation-control" (sensing-communication-computation-control).

[0025] Existing resource allocation research is generally communication-oriented, aiming to optimize communication performance indicators such as throughput, latency, and spectral efficiency. However, traditional solutions neglect the information processing capabilities of the receiver, assuming that these capabilities are unlimited. In actual low-altitude operation scenarios, each UAV has limited onboard processors, cache space, and control command parsing capabilities. This means that within a unit control cycle, there is a physical upper limit to the amount of information that each UAV can receive, decode, and effectively use for control execution. If the amount of transmitted information exceeds the UAV's operational capacity, the excess information cannot be processed in time, leading not only to resource waste but also to command backlog, control delays, and even UAV behavioral chaos or system instability. Especially in closed-loop systems, control performance depends not only on the amount of information transmitted via the downlink but also on whether the UAV can process and execute this information within the correct timeframe. Traditional "water-filling" algorithms may allocate excessive power to UAVs with good channel conditions but weak processing capabilities, causing them to overload, while UAVs with strong processing capabilities but slightly poor channel conditions do not receive sufficient resources. The overall system performance may be limited by the operational capabilities of individual UAVs, and simply increasing transmission power cannot overcome this bottleneck.

[0026] In view of this, this application provides a closed-loop system resource orchestration method for low-altitude UAV formation operations. It comprehensively considers the total power resource constraints of the closed-loop system as well as the different channel conditions and working capabilities among the UAVs, and determines the power allocation of multiple UAVs performing the same control task. This ensures that the allocated resources can be supported by the channel and effectively utilized by the UAVs, ultimately achieving the optimal overall performance of the closed-loop control system.

[0027] The following detailed description, with reference to the accompanying drawings, provides a closed-loop system resource orchestration method, system, and medium for low-altitude UAV swarm operations based on this application. Unless otherwise specified, the features described in the following embodiments and implementations can be combined with each other.

[0028] Assumption Figure 1 The closed-loop system shown contains K unmanned aerial vehicles (UAVs) performing the same control task, and the sub-channels are mutually orthogonal. The process of multiple UAVs performing the same control task is modeled as a discrete linear time-invariant system, and the state transition matrix of the discrete linear time-invariant system is: And these are known parameters.

[0029] Let T be the available transmission time of control commands in a sensor-computer-control closed loop. Let the sub-channel bandwidth for transmitting control commands from the command center to the k-th operational UAV be... The maximum transmission power constraint of the command center is The maximum amount of data (i.e., workload) that the k-th operational drone can receive and process within one cycle is limited to: Assuming both the operational drone and the user communication module at the command center are equipped with a single antenna, the channel capacity between the k-th operational drone and the command center is... It can be represented as: (1) in, Represents the mathematical expectation. The transmission power for the command center to transmit control commands to the k-th operational UAV. Let K be the communication noise variance, and K be the number of operational drones. Additionally, let K be the channel gain between the k-th operational drone and the command center. Represented as: in, The rapidly changing small-scale channel gain is modeled as Rayleigh fading in this application. The large-scale channel gain is expressed as follows: In the formula This represents the distance between the k-th operational drone and the command center. This is the path loss index. This indicates the shadow fading effect.

[0030] Due to small-scale channel gain It is not a fixed value. Research has shown that in order to remove the expected integral operation of small-scale fading, a mathematical optimization auxiliary variable can be artificially introduced, namely the comprehensive coupling coefficient of effective signal + interference + noise, i.e., the comprehensive coupling coefficient of signal-to-interference-plus-noise ratio w. Its function is to transform the non-convex power allocation optimization problem into a convex optimization problem, thereby improving the small-scale channel gain in the above formula (1). Therefore, the channel capacity between the k-th operational drone and the command center is reduced. It can be approximated as: (2) in, Let be the signal-to-interference-plus-noise ratio (SIR) and coupling coefficient of the k-th channel. .

[0031] In the field of control, the greater the amount of information that ultimately affects the control task, the better the system's control performance. Therefore, this application uses the amount of control command information R that ultimately affects the task from the command center to measure the overall control performance of the sensing-transfer-computer-control closed loop.

[0032] Based on this, this application provides a closed-loop system resource orchestration method for low-altitude UAV formation operations. Figure 2 A flowchart illustrating a closed-loop system resource orchestration method for low-altitude UAV swarm operations according to an embodiment of this application is disclosed. Figure 2 As shown, a closed-loop system resource orchestration method for low-altitude UAV formation operations according to an embodiment of this application may include steps S1 to S3.

[0033] In step S1, the power allocation optimization problem of multiple operational UAVs performing the same control task in the downlink of the closed-loop system is transformed into a convex optimization problem of the amount of control command information that ultimately affects the operational task from the command center of the closed-loop system.

[0034] In step S2, the constraints of the convex optimization problem are determined based on the maximum transmission power of the command center and the downlink channel conditions and processing capabilities of each UAV in the collaborative operation.

[0035] In step S3, under constraints, the convex optimization problem is iteratively solved to determine the optimal transmission power allocation for multiple operational UAVs to perform the same control task.

[0036] The convex optimization problem of the amount of control command information that ultimately affects the operation task from the command center uses the transmission power of the control commands transmitted from the command center to the operation UAV and the signal-to-interference-plus-noise ratio combined coupling coefficient as optimization variables. In some embodiments, the closed-loop system resource orchestration method for low-altitude UAV formation operations of this application may further include step S4.

[0037] In step S4, the convex optimization problem of control command information quantity is decomposed into a first convex optimization subproblem and a second convex optimization subproblem of control command information quantity. The first convex optimization subproblem uses the transmission power of the control commands transmitted from the command center to the operational UAV as the optimization variable, and maximizing the control command information quantity as the objective function. The second convex optimization subproblem uses the signal-to-interference-plus-noise ratio (SINR) combined coupling coefficient as the optimization variable, and minimizing the control command information quantity as the objective function.

[0038] In this case, the iterative solution of the convex optimization problem in step S3 to determine the optimal transmission power allocation for multiple operational UAVs to perform the same control task may include: determining the optimal transmission power allocation for multiple operational UAVs to perform the same control task by alternately iteratively solving the first convex optimization subproblem of the control command information quantity and the second convex optimization subproblem of the control command information quantity.

[0039] When the process of multiple operational drones performing the same control task is modeled as a discrete linear time-invariant system, the state transition matrix of the discrete linear time-invariant system can be used as a reference. The intrinsic entropy rate of the discrete linear time-invariant system is obtained.

[0040] Among them, the control command information R that ultimately affects the operation task from the command center must satisfy the following condition with the intrinsic entropy rate of the discrete linear time-invariant system: In the formula, Let be the intrinsic entropy rate of a discrete linear time-invariant system.

[0041] The channel capacity between the kth operational UAV and the command center can be used as a reference. The amount of control command information that affects the operation task by the k-th UAV is determined by the available transmission time T of the control commands in the closed-loop system. The upper bound of the range, where the channel capacity between the k-th operational drone and the command center is... The expression can be shown in formula (2) above. Then, the amount of control command information that affects the operation task by the kth operation drone can be used as a basis. To determine the amount of control instruction information R that ultimately affects the operational task from the command center.

[0042] Assuming that the channels of each operational UAV are orthogonal to each other, we can obtain the amount of control command information R that ultimately affects the operational task from the command center and the amount of control command information that affects the operational task from the kth operational UAV. The relationship between them is as follows: In the formula, K represents the number of operational drones.

[0043] Among them, the control command information quantity R that ultimately affects the operation task from the command center and the condition required for the intrinsic entropy rate of the discrete linear time-invariant system, as well as the control command information quantity R that ultimately affects the operation task from the command center and the control command information quantity that affects the operation task from the k-th operation UAV. The relationships between them are all within the constraints of the convex optimization problem of controlling the amount of instruction information.

[0044] In some embodiments, the specific steps of the closed-loop system resource orchestration method for low-altitude UAV formation operations of this application are as follows: First, the original optimization problem can be transformed and decomposed into two convex optimization subproblems: The original optimization problem P1 is as follows: P1: (3) (4) (5) (6) (7) (8) (9) (10) The first convex optimization subproblem PA is denoted as: (11) The second convex optimization subproblem PB is denoted as: (12) Then, the first convex optimization subproblem PA and the second convex optimization subproblem PB are solved iteratively, alternating between them: i. Set the convergence threshold Initialize the number of iterations , , ; ii. Fixed signal-to-interference-plus-noise ratio (SINR) combined coupling coefficient Solve the first convex optimization subproblem PA to obtain the updated transmission power of the operational UAV for transmitting control commands. ; iii. Fixed transmission power of the updated operational UAV for transmitting control commands Solve the second convex optimization subproblem to obtain the updated signal-to-interference-plus-noise ratio (SIR) combined coupling coefficient. ; iv. Calculate the norm of the variable update. ,like If the algorithm converges, then the optimal transmission power allocation is recorded. The amount of control command information that ultimately affects the operational tasks from the command center. Proceed to the next step v; otherwise, let , , Return to step ii and continue the alternating iteration; v. Output optimal transmission power allocation and the amount of control command information from the command center that ultimately affects the operational tasks. .

[0045] Optionally, the fixed signal-to-interference-plus-noise ratio (SINNR) combined coupling coefficient in step ii The closed-form solution to the first convex optimization subproblem can be expressed as: in, It is the only water level parameter that satisfies the maximum transmission power constraint.

[0046] In some embodiments, the closed-loop system resource orchestration method for low-altitude UAV formation operations of this application may further include steps S51 to S53.

[0047] In step S51, the amount of control command information that ultimately affects the operation task can be based on the amount of information from the command center. intrinsic entropy rate of discrete linear time-invariant systems The magnitude of the value determines the stability of the closed-loop system.

[0048] In step S52, if If so, the closed-loop system is stable and can operate normally.

[0049] In step S53, if If this happens, the closed-loop system will be unstable and cannot operate normally.

[0050] The closed-loop system resource orchestration method for low-altitude UAV swarm operations proposed in this application comprehensively considers the downlink channel conditions and differentiated processing capabilities of each UAV in collaborative operations. Under the premise of strictly meeting the working capacity limitations of each UAV, it maximizes the overall control performance of collaborative tasks by optimizing power resource allocation, thereby improving system resource utilization efficiency and task reliability.

[0051] Furthermore, the closed-loop system resource orchestration method for low-altitude UAV formation operations proposed in this application can decompose the complex problem of power allocation optimization into two convex optimization sub-problems, and maximize the control performance of the closed-loop system under limited resources through an alternating iterative optimization algorithm.

[0052] The following is a specific example of the closed-loop system resource orchestration method for low-altitude UAV formation operations proposed in this application.

[0053] The closed-loop system resource orchestration method for low-altitude UAV formation operations proposed in this application is applied to, for example... Figure 1 In the closed-loop system shown, five operational drones are randomly and uniformly distributed in a circle with a radius of 5 km and an altitude of 1 km. The command center is located on the ground at the center of the circle. Let the bandwidth of each sub-channel be... Shadow fading effect Path loss index Communication noise variance Limitations of the working capabilities of each drone Randomly generated from [0, 1500].

[0054] Assuming the available transmission time of control commands in the closed-loop system is T = 49.8 ms, and the intrinsic entropy rate of the discrete linear time-invariant system is... Set the convergence threshold to 2000. .

[0055] Under the above simulation conditions, this example performs point-by-point simulation with downlink transmission power constraints ranging from 1dBW to 15dBW at 2dBW intervals to obtain the amount of control command information that ultimately affects the operation task. The performance of the proposed scheme is then compared with that of traditional water injection schemes and power sharing schemes. Figure 3 The diagram shows a performance comparison between the proposed solution and traditional water injection and power-sharing solutions. (See diagram for example.) Figure 3 As shown, the curve marked by the red circle represents the simulation results of the proposed solution. It can be seen that the proposed solution can effectively improve the control performance of the system. Figure 4 An iteration diagram of a closed-loop system resource orchestration method for low-altitude UAV formation operations, according to an embodiment of this application, is disclosed. For example... Figure 4 As shown, the solution in this application has low-complexity iterative implementation and can be solved quickly.

[0056] Therefore, it is evident that the proposed solution can significantly improve the control performance of the closed-loop system compared to existing traditional water injection and power-sharing schemes. Furthermore, the proposed solution has low complexity, can be implemented using low-complexity iterative methods, and can be solved efficiently and quickly.

[0057] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the closed-loop system resource orchestration method for low-altitude UAV formation operations described above.

[0058] This application also provides a closed-loop system resource orchestration system 500 for low-altitude UAV formation operations. Figure 5 A schematic block diagram of a closed-loop system resource orchestration system 500 for low-altitude UAV swarm operations, according to one embodiment of this application, is shown. Figure 5 As shown, an embodiment of the closed-loop system resource orchestration system 500 for low-altitude UAV formation operations according to this application includes a processor 501, an internal bus 502, a network interface 503, a memory 504, and a non-volatile memory 505. It may also include other hardware required for other operations. The processor 501 can read the corresponding computer program from the non-volatile memory 505 into the memory 504 and then run it to implement the steps of the closed-loop system resource orchestration method for low-altitude UAV formation operations as described above. Of course, besides software implementation, this application does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution entity of the following processing flow is not limited to individual logic units, but can also be hardware or logic components.

[0059] The closed-loop system resource orchestration system 500 for low-altitude UAV formation operations described in this application has similar beneficial technical effects to the closed-loop system resource orchestration method for low-altitude UAV formation operations described above, therefore, it will not be repeated here.

[0060] The foregoing has provided a detailed description of the closed-loop system resource orchestration method, system, and medium for low-altitude UAV formation operations provided in the embodiments of this application. Specific examples have been used to illustrate the closed-loop system resource orchestration method, system, and medium for low-altitude UAV formation operations in this document. The descriptions of the embodiments above are merely for the purpose of helping to understand the core ideas of this application and are not intended to limit this application. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from the spirit and principles of this application, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A closed-loop system resource orchestration method for low-altitude UAV formation operations, characterized in that, The method includes: The power allocation optimization problem of multiple operational UAVs performing the same control task in the downlink of the closed-loop system is transformed into a convex optimization problem of the amount of control command information that ultimately affects the operational task from the command center of the closed-loop system. The constraints of the convex optimization problem are determined based on the maximum transmission power of the command center and the downlink channel conditions and processing capabilities of each UAV in the collaborative operation. Under the constraints, the convex optimization problem is solved iteratively to determine the optimal transmission power allocation for multiple operational UAVs performing the same control task.

2. The method as described in claim 1, characterized in that, The convex optimization problem of the control command information quantity uses the transmission power of the control command transmitted from the command center to the operational UAV and the signal-to-interference-plus-noise ratio (SIR) of the k-th channel, which is introduced to transform the non-convex power allocation optimization problem into a convex optimization problem, as optimization variables. The method further includes: The convex optimization problem of the control command information quantity is decomposed into a first convex optimization subproblem and a second convex optimization subproblem of the control command information quantity. The first convex optimization subproblem of the control command information quantity uses the transmit power as the optimization variable and maximizes the control command information quantity as the objective function. The second convex optimization subproblem of the control command information quantity uses the signal-to-interference-plus-noise ratio (SINR) combined coupling coefficient as the optimization variable and minimizes the control command information quantity as the objective function. The iterative solution of the convex optimization problem to determine the optimal transmission power allocation for multiple UAVs performing the same control task includes: The optimal transmission power allocation is determined by alternately and iteratively solving the first convex optimization subproblem and the second convex optimization subproblem of the control command information.

3. The method as described in claim 1, characterized in that, The method includes: The process of multiple operational drones performing the same control task is modeled as a discrete linear time-invariant system. Based on the state transition matrix of the discrete linear time-invariant system The intrinsic entropy rate of the discrete linear time-invariant system is obtained, where, The constraints of the convex optimization problem include: , In the formula, R represents the amount of control command information that ultimately affects the operation task from the command center; Let be the intrinsic entropy rate of the discrete linear time-invariant system.

4. The method as described in claim 3, characterized in that, The method further includes: Based on the channel capacity between the kth operational drone and the command center The amount of control command information that affects the operation task by the k-th UAV is determined by the available transmission time T of the control commands in the closed-loop system. The upper bound; Based on the amount of control command information that affects the task by the kth operational drone To determine the amount of control instruction information R that ultimately affects the operational task from the command center.

5. The method as described in claim 4, characterized in that, Channel capacity between the kth operational drone and the command center The expression is: , in, Let be the signal-to-interference-plus-noise ratio (SIR) and coupling coefficient of the k-th channel. ; The sub-channel bandwidth for transmitting control commands from the command center to the k-th operational UAV; The transmission power for the command center to transmit control commands to the k-th operational UAV; For communication noise variance; The large-scale channel gain is expressed as follows: In the formula Indicates the first The distance between the operational drones and the command center This is the path loss index. This represents the shadow fading effect; K is the number of operational drones.

6. The method as described in claim 4, characterized in that, The method further includes: The channels of each operating drone are set to be orthogonal to each other. The constraints of the convex optimization problem also include: , In the formula, K represents the number of operational drones.

7. The method as described in claim 5, characterized in that, The constraints of the convex optimization problem also include: , , , , , in, This represents the maximum transmission power of the command center. This represents the maximum amount of data that the k-th operational drone can receive and process within one cycle.

8. The method as described in claim 7, characterized in that, The step of determining the optimal transmission power allocation by alternately iteratively solving the first convex optimization subproblem and the second convex optimization subproblem of the control command information includes: Set convergence threshold Initialize the number of iterations , , ; Fixed signal-to-interference-plus-noise ratio (SINR) combined coupling coefficient Solve the first convex optimization subproblem to obtain the updated transmission power of the operational UAV for transmitting control commands. ; Fixed update of the transmission power of the operation drone for transmitting control commands Solve the second convex optimization subproblem to obtain the updated signal-to-interference-plus-noise ratio (SIR) combined coupling coefficient. ; Calculate the norm of the variable update. ,like If the algorithm converges, then the optimal transmission power allocation is recorded. The amount of control command information that ultimately affects the operational tasks from the command center. If so, proceed to the next step; otherwise, let , , Return to fixed The steps to solve the first convex optimization subproblem; Output optimal transmission power allocation and the amount of control command information from the command center that ultimately affects the operational tasks. .

9. The method as described in claim 8, characterized in that, Fixed signal-to-interference-plus-noise ratio (SINR) combined coupling coefficient The closed-form solution to the first convex optimization subproblem is: , in, It is the only water level parameter that satisfies the maximum transmission power constraint.

10. The method as described in claim 8, characterized in that, The method further includes: Based on the amount of control command information that ultimately affects the operational tasks from the command center With respect to the intrinsic entropy rate of the discrete linear time-invariant system The magnitude of the value determines the stability of the closed-loop system; like If the closed-loop system is stable, it can operate normally. like If this happens, the closed-loop system will be unstable and cannot operate normally.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the closed-loop system resource orchestration method for low-altitude UAV formation operations as described in any one of claims 1 to 10.

12. A closed-loop system resource orchestration system for low-altitude UAV formation operations, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the closed-loop system resource orchestration method for low-altitude UAV formation operations as described in any one of claims 1 to 10.

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