1. The utility model provides a study and resource joint optimization method towards unmanned aerial vehicle cluster federal study, the unmanned aerial vehicle cluster comprises K unmanned aerial vehicle and a leading unmanned aerial vehicle, its characterized in that specifically includes the following step:

(1) collecting K pictures of different angles shot by the unmanned aerial vehicle in the patrol process, and taking the pictures as a local data set;

(2) obtaining the calculation energy consumption and the calculation time delay required by the following unmanned aerial vehicle in one round of global training according to the CPU frequency, the local training round and the size of the local data set of the following unmanned aerial vehicle;

(3) calculating communication energy consumption and communication time delay of one round of global training of the following unmanned aerial vehicle according to a space path loss model between the following unmanned aerial vehicle and the leading unmanned aerial vehicle;

(4) minimizing the total training energy consumption of the unmanned aerial vehicle cluster according to the calculation time delay, the communication time delay and the global convergence precision, wherein the total training energy consumption comprises the following steps: the total calculation energy consumption of the K frames following the unmanned aerial vehicle and the total communication energy consumption of the K frames following the unmanned aerial vehicle; the method specifically comprises the following substeps:

(4.1) optimizing the local convergence precision by a successive convex approximation method according to the constraint of the local training round, the constraint of the local convergence precision and the constraint that the time of each round of global training is less than the maximum training time delay;

(4.2) optimizing the local training round according to the constraint of the local training round and the constraint that the time of each round of global training is less than the maximum training time delay;

(4.3) optimizing and calculating frequency allocation and bandwidth allocation by using a CVX solving tool according to the constraint of the total bandwidth, the constraint of the local training round, the local convergence precision, the unmanned aerial vehicle CPU calculation frequency and the constraint of the value interval of the sub-bandwidth;

(4.4) repeating the iteration steps (4.1) - (4.3) until the relative error of the total training energy consumption of the unmanned aerial vehicle cluster is smaller than 0.01, so as to obtain the minimized total training energy consumption of the unmanned aerial vehicle cluster, and obtain the optimal local convergence precision, local training turns, the CPU frequency following the unmanned aerial vehicle and the sub bandwidth following the unmanned aerial vehicle;

(5) inputting the optimal local convergence precision, the local training turn, the CPU frequency of the following unmanned aerial vehicle and the sub bandwidth of the following unmanned aerial vehicle into the corresponding following unmanned aerial vehicle, and performing routing inspection task by the following unmanned aerial vehicle following the leading unmanned aerial vehicle.

2. The unmanned aerial vehicle cluster federal learning oriented learning and resource joint optimization method as claimed in claim 1, wherein the calculated energy consumption isThe method specifically comprises the following steps:

；

said calculating time delayThe method specifically comprises the following steps:

；

wherein N (t) represents the local training round performed in the t-th round of global training,which represents the effective capacitance coefficient of the capacitor,indicating the number of CPU cycles required for the kth rack to follow each data sample in the local dataset for the drone,Representing the number of data samples in the local data set for the kth rack following drone k,indicating that the kth rack follows the drone's CPU frequency.

3. The unmanned aerial vehicle cluster federal learning oriented learning and resource joint optimization method as claimed in claim 1, wherein the communication energy consumption isThe method specifically comprises the following steps:

；

the communication time delayThe method specifically comprises the following steps:

；

wherein the content of the first and second substances,indicating the transmission power of the kth rack following the drone,indicating the amount of data that the following drone needs to upload to the lead drone,indicating that the kth shelf follows the sub-bandwidth to which the drone is assigned,the channel gain per meter is expressed in terms of,which is indicative of the power of the noise,indicating the distance between the kth following drone and the leading drone.

4. The unmanned aerial vehicle cluster federal learning-oriented resource joint optimization method as claimed in claim 1, wherein the constraints of the local training round are specifically:

wherein the content of the first and second substances,Lexpressing the Lipschitz constant, gamma expressing the strong convexity coefficient, lambda expressing the learning rate of the local training, t expressing the serial number of the global training round, N (t) expressing the local training round of the t-th global training,indicating the local convergence accuracy of the t-th round of global training,representing the number of rounds of global training.

5. The unmanned aerial vehicle cluster federal learning-oriented learning and resource joint optimization method as claimed in claim 1, wherein the constraint on the local convergence accuracy specifically is:

wherein t represents the sequence number of the global training round,indicating the local convergence accuracy of the t-th round of global training,Lrepresents a Lipschitz constant, gamma represents a strong convexity coefficient, and ξ is a correlation coefficient and satisfies，Which indicates the accuracy of the global convergence,representing the number of rounds of global training.

6. The unmanned aerial vehicle cluster federal learning-oriented resource joint optimization method as claimed in claim 1, wherein the constraint that the time of each global training is less than the maximum training delay is specifically as follows:

wherein t represents the sequence number of the global training round, N (t) represents the local training round of the t-th global training,which means that the time delay is calculated,which is indicative of the time delay of the communication,the maximum training time delay is indicated by the time,representing the number of rounds of global training.

7. The unmanned aerial vehicle cluster federal learning-oriented resource joint optimization method as claimed in claim 1, wherein the constraint on the total bandwidth specifically is:

wherein t represents the sequence number of the global training round,the sub-bandwidth allocated by the k-th frame following the unmanned aerial vehicle in the t-th round of global training is shown, and B is the total bandwidth.

8. The method for learning and resource joint optimization for unmanned aerial vehicle cluster federal learning according to claim 1, wherein the constraints on the local convergence accuracy, the unmanned aerial vehicle CPU computation frequency and the value intervals of the sub-bandwidths are specifically:

wherein t represents the sequence number of the global training round,indicating the local training accuracy of the global training at the t-th round,indicating the calculation frequency of the kth frame following unmanned aerial vehicle in the t-th round global training and indicating that the kth frame follows the unmanned aerial vehicleRepresents the sub-bandwidth allocated to the kth frame following drone k in the t-th round of global training,representing the number of rounds of global training.

9. The unmanned aerial vehicle cluster federal learning oriented learning and resource joint optimization method as claimed in claim 1, wherein the updating process of the local convergence accuracy in step (4.1) is as follows:

wherein the content of the first and second substances,the local convergence accuracy of the ith round of iteration is indicated,indicating the local convergence accuracy of the (i + 1) th iteration,the local convergence accuracy obtained by the solution is shown,indicating the step size of the update.

Background

With the development of the unmanned aerial vehicle technology, the unmanned aerial vehicle is widely applied to military and civil fields, such as post-disaster search and rescue, target tracking, mountain fire detection and the like. In order to be able to autonomously execute these applications, a cluster of drones, consisting of multiple drones, needs to have the ability to make real-time decisions through intelligent analysis. Machine learning, a rapidly growing intelligent technology in recent years, enables the ability of unmanned aerial vehicle clusters to possess intelligent analysis, thereby executing more emerging applications.

The traditional centralized machine learning scheme needs to transmit raw data to a central cloud server for processing, and the centralized scheme cannot be directly applied to an unmanned aerial vehicle cluster due to the following three reasons: (1) transmitting raw data in limited bandwidth resources results in excessive energy consumption; (2) for such delay-effective applications of target tracking, high delays caused by the transmission of large amounts of data are unacceptable; (3) the direct transmission of raw data may cause the leakage of sensitive information, such as identity and location information of the drone.

Compared with the traditional centralized machine learning, the federal learning is more suitable for the unmanned aerial vehicle cluster as a distributed machine learning without transmitting original data. Through federal learning, the following unmanned aerial vehicle can use the collected data to carry out local training and upload local model parameters to the leading unmanned aerial vehicle for model aggregation, so that the original data with sensitive information does not need to be uploaded. However, deployment of federal learning in drone clusters also presents some challenges due to limited battery capacity of the drones, instability of the wireless channel, etc., which need to be addressed by configuring appropriate training parameters and reasonable resource allocation.

In order to perform efficient federal learning in an unmanned aerial vehicle cluster network with limited airborne resources and network resources, learning and resource allocation of federal learning are optimized in a combined manner, and therefore it is very important to minimize the total training energy consumption of an unmanned aerial vehicle cluster while ensuring the constraint of global training precision and maximum training time delay.

Disclosure of Invention

The invention aims to provide a learning and resource joint optimization method facing unmanned aerial vehicle cluster federal learning, aiming at the defects or problems in the prior art.

In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a learning and resource joint optimization method for federal learning of unmanned aerial vehicle cluster is disclosed, wherein the unmanned aerial vehicle cluster is composed of K unmanned aerial vehicles and a leading unmanned aerial vehicle, and the method specifically comprises the following steps:

(1) collecting K pictures of different angles shot by the unmanned aerial vehicle in the patrol process, and taking the pictures as a local data set;

(2) obtaining the calculation energy consumption and the calculation time delay required by the following unmanned aerial vehicle in one round of global training according to the CPU frequency, the local training round and the size of the local data set of the following unmanned aerial vehicle;

(3) calculating communication energy consumption and communication time delay of one round of global training of the following unmanned aerial vehicle according to a space path loss model between the following unmanned aerial vehicle and the leading unmanned aerial vehicle;

(4) minimizing the total training energy consumption of the unmanned aerial vehicle cluster according to the calculation time delay, the communication time delay and the global convergence precision, wherein the total training energy consumption comprises the following steps: the total calculation energy consumption of the K frames following the unmanned aerial vehicle and the total communication energy consumption of the K frames following the unmanned aerial vehicle; the method specifically comprises the following substeps:

(4.1) optimizing the local convergence precision by a successive convex approximation method according to the constraint of the local training round, the constraint of the local convergence precision and the constraint that the time of each round of global training is less than the maximum training time delay;

(4.2) optimizing the local training round according to the constraint of the local training round and the constraint that the time of each round of global training is less than the maximum training time delay;

(4.3) optimizing and calculating frequency allocation and bandwidth allocation by using a CVX solving tool according to the constraint of the total bandwidth, the constraint of the local training round, the local convergence precision, the unmanned aerial vehicle CPU calculation frequency and the constraint of the value interval of the sub-bandwidth;

(4.4) repeating the iteration steps (4.1) - (4.3) until the relative error of the total training energy consumption of the unmanned aerial vehicle cluster is smaller than 0.01, so as to obtain the minimized total training energy consumption of the unmanned aerial vehicle cluster, and obtain the optimal local convergence precision, local training turns, the CPU frequency following the unmanned aerial vehicle and the sub bandwidth following the unmanned aerial vehicle;

(5) inputting the optimal local convergence precision, the local training turn, the CPU frequency of the following unmanned aerial vehicle and the sub bandwidth of the following unmanned aerial vehicle into the corresponding following unmanned aerial vehicle, and performing routing inspection task by the following unmanned aerial vehicle following the leading unmanned aerial vehicle.

Further, the calculated energy consumptionThe method specifically comprises the following steps:

；

said calculating time delayThe method specifically comprises the following steps:

；

wherein N (t) represents the local training round performed in the t-th round of global training,which represents the effective capacitance coefficient of the capacitor,indicating the number of CPU cycles required for the kth rack to follow each data sample in the local dataset for the drone,Representing the number of data samples in the local data set for the kth rack following drone k,indicating that the kth rack follows the drone's CPU frequency.

Further, the communication energy consumptionThe method specifically comprises the following steps:

；

the communication time delayThe method specifically comprises the following steps:

；

wherein the content of the first and second substances,indicating the transmission power of the kth rack following the drone,indicating the amount of data that the following drone needs to upload to the lead drone,indicating that the kth shelf follows the sub-bandwidth to which the drone is assigned,the channel gain per meter is expressed in terms of,which is indicative of the power of the noise,indicating the distance between the kth following drone and the leading drone.

Further, the constraint of the local training round is specifically:

wherein the content of the first and second substances,Lexpressing the Lipschitz constant, gamma expressing the strong convexity coefficient, lambda expressing the learning rate of the local training, t expressing the serial number of the global training round, N (t) expressing the local training round of the t-th global training,indicating the local convergence accuracy of the t-th round of global training,representing the number of rounds of global training.

Further, the constraint of the local convergence accuracy specifically includes:

wherein t represents the sequence number of the global training round,indicating the local convergence accuracy of the t-th round of global training,Lrepresents a Lipschitz constant, gamma represents a strong convexity coefficient, and ξ is a correlation coefficient and satisfies，Which indicates the accuracy of the global convergence,representing the number of rounds of global training.

Further, the constraint that the time of each round of global training is less than the maximum training delay specifically includes:

wherein t represents the sequence number of the global training round, N (t) represents the local training round of the t-th global training,which means that the time delay is calculated,which is indicative of the time delay of the communication,the maximum training time delay is indicated by the time,representing the number of rounds of global training.

Further, the constraint of the total bandwidth specifically includes:

wherein t represents the sequence number of the global training round,the sub-bandwidth allocated by the k-th frame following the unmanned aerial vehicle in the t-th round of global training is shown, and B is the total bandwidth.

Further, the constraints of the local convergence precision, the unmanned aerial vehicle CPU computation frequency, and the value intervals of the sub-bandwidths are specifically:

wherein t represents the sequence number of the global training round,indicating the local training accuracy of the global training at the t-th round,indicating the calculated frequency of the kth frame following the drone in the tth round of global training,representing the maximum calculated frequency of the kth carriage following the drone,indicating that the kth shelf follows the sub-bandwidth allocated to drone k in the tth round of global training,representing the number of rounds of global training.

Further, the updating process of the local convergence accuracy in the step (4.1) is as follows:

wherein the content of the first and second substances,the local convergence accuracy of the ith round of iteration is indicated,indicating the local convergence accuracy of the (i + 1) th iteration,the local convergence accuracy obtained by the solution is shown,indicating the step size of the update.

Compared with the prior art, the invention has the following beneficial effects: the invention relates to a method for learning and resource joint optimization for unmanned aerial vehicle cluster federal learning, which takes the total training energy consumption of an unmanned aerial vehicle cluster as a target under the condition of considering global convergence precision and time delay constraint, decomposes the minimization problem of the total training energy consumption of the unmanned aerial vehicle cluster into three sub-problems according to the alternative optimization idea, and simultaneously optimizes the local convergence precision, the local training round, the calculation resource allocation and the bandwidth allocation through a joint optimization method of successive convex approximation and joint optimization of resource allocation, thereby minimizing the total training energy of the unmanned aerial vehicle cluster according to the calculation time delay, the communication time delay and the global convergence precision. The method of the invention realizes the following of the unmanned aerial vehicle and the leading of the unmanned aerial vehicle to carry out the routing inspection task, realizes the characteristic of intellectualization, greatly improves the working efficiency and reduces the labor cost.

Drawings

Fig. 1 is a federal learning scenario diagram facing an unmanned aerial vehicle cluster, provided by the invention;

FIG. 2 is a simulation result diagram of the total training energy consumption of the unmanned aerial vehicle cluster under different global convergence accuracies;

FIG. 3 is a diagram of a simulation result of a relationship experiment of total training energy consumption and total bandwidth of an unmanned aerial vehicle cluster under different algorithms;

FIG. 4 is a diagram of a simulation result of a relationship experiment of total training energy consumption of an unmanned aerial vehicle cluster and a maximum global time delay under different algorithms;

fig. 5 is a diagram of a simulation result of a relationship experiment of total training energy consumption and data volume of an unmanned aerial vehicle cluster under different algorithms.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.

The invention provides a method for learning and resource joint optimization facing unmanned aerial vehicle cluster federal learning, wherein an unmanned aerial vehicle cluster consists of K following unmanned aerial vehicles and a leading unmanned aerial vehicle, the K following unmanned aerial vehicles and the leading unmanned aerial vehicle carry out patrol tasks according to a preset air route, and the unmanned aerial vehicle cluster is subjected to federal learning in the patrol process, so that joint optimization is carried out on local convergence precision, local training turns, calculation resource distribution and bandwidth distribution, and the total training energy consumption of the unmanned aerial vehicle cluster is minimized according to calculation delay, communication delay and global convergence precision. In the process of federal learning, due to the fact that the battery capacity of the unmanned aerial vehicle is limited, the optimization problem of minimizing the total training energy consumption of the unmanned aerial vehicle cluster exists, and the method specifically has the following challenges: 1) in the federal learning of the unmanned aerial vehicle cluster, reasonable learning parameters such as local convergence accuracy and local training turns need to be configured, and too few local training turns may cause that the corresponding global convergence accuracy cannot be achieved; 2) in order to reduce training energy consumption, the time delay of training by adopting lower CPU (central processing unit) calculation frequency along with the unmanned aerial vehicle may exceed the maximum global time delay constraint, so that the timeliness of training cannot be guaranteed; 3) because the distance between every following unmanned aerial vehicle and the leading unmanned aerial vehicle is different, need distribute reasonable sub-bandwidth and guarantee every following unmanned aerial vehicle's smooth upload of local model parameter.

Fig. 1 is a federal learning scene diagram facing an unmanned aerial vehicle cluster, wherein the unmanned aerial vehicle cluster is composed of K following unmanned aerial vehicles and a leading unmanned aerial vehicle, and performs a federal learning training task while flying according to a predetermined route. Firstly, carrying out local training by using a local data set by a K frame following unmanned aerial vehicle; then, the K frame follows the unmanned aerial vehicle to upload the local model parameters obtained by training to a leading unmanned aerial vehicle serving as a parameter server; and finally, the leading unmanned aerial vehicle performs weighted summation on the received local models to generate new global model parameters, and the new global model parameters are issued to each following unmanned aerial vehicle to start a new round of global training. The invention relates to a learning and resource joint optimization method for unmanned aerial vehicle cluster federal learning, which specifically comprises the following steps:

(1) collecting K pictures of different angles shot by the unmanned aerial vehicle in the patrol process, and taking the pictures as a local data set;

(2) obtaining the calculation energy consumption and the calculation time delay required by the following unmanned aerial vehicle in one round of global training according to the CPU frequency, the local training round and the size of the local data set of the following unmanned aerial vehicle;

calculating energy consumption in the inventionThe method specifically comprises the following steps:

；

calculating time delay in the inventionThe method specifically comprises the following steps:

；

wherein N (t) represents the local training round performed in the t-th round of global training,which represents the effective capacitance coefficient of the capacitor,indicating the number of CPU cycles required for the kth rack to follow each data sample in the local dataset for the drone,Representing the number of data samples in the local data set for the kth rack following the drone,indicating that the kth rack follows the drone's CPU frequency.

(3) Calculating communication energy consumption and communication time delay of one round of global training of the following unmanned aerial vehicle according to a space path loss model between the following unmanned aerial vehicle and the leading unmanned aerial vehicle;

communication energy consumption in the inventionThe method specifically comprises the following steps:

；

obtaining the communication time delay of a round of global training through the communication time delay and the transmission powerExpressed as:

；

wherein the content of the first and second substances,indicating the transmission power of the kth rack following the drone,indicating the amount of data that the following drone needs to upload to the lead drone,indicating that the kth shelf follows the sub-bandwidth to which the drone is assigned,the channel gain per meter is expressed in terms of,which is indicative of the power of the noise,indicating the distance between the kth following drone and the leading drone.

(4) Minimizing the total training energy consumption of the unmanned aerial vehicle cluster according to the calculation time delay, the communication time delay and the global convergence precision, wherein the total training energy consumption comprises the following steps: the total calculation energy consumption of the K frames following the unmanned aerial vehicle and the total communication energy consumption of the K frames following the unmanned aerial vehicle; the method reduces the solving difficulty of minimizing the total training energy consumption of the unmanned aerial vehicle cluster by decomposing the original mixed integer non-convex problem into the optimization of three sub-problems, including the optimization of local convergence precision, the optimization of local training rounds, the optimization of calculation frequency distribution and bandwidth distribution, and specifically comprises the following sub-steps:

(4.1) under the condition of fixing the local training round, calculating frequency allocation and bandwidth allocation, converting the original problem into a non-convex problem and solving a local optimal solution of local convergence precision by utilizing successive convex approximation, so that the solving difficulty of the original problem is greatly reduced, and the local convergence precision is optimized by a successive convex approximation method according to the constraint of the local training round, the constraint of the local convergence precision and the constraint that the time of each round of global training is less than the maximum training time delay; the updating process of the local convergence precision in the invention specifically comprises the following steps:

wherein the content of the first and second substances,the local convergence accuracy of the ith round of iteration is indicated,indicating the local convergence accuracy of the (i + 1) th iteration,the local convergence accuracy obtained by the solution is shown,the step size of the update can be adjustedIs controlled by the size ofThe speed of the iterative update.

The constraints of the local training rounds involved in the process are specifically as follows:

wherein the content of the first and second substances,Lexpressing the Lipschitz constant, gamma expressing the strong convexity coefficient, lambda expressing the learning rate of the local training, t expressing the serial number of the global training round, N (t) expressing the local training round of the t-th global training,indicating the local convergence accuracy of the t-th round of global training,representing the number of rounds of global training.

The constraint of the local convergence accuracy is specifically:

wherein t represents the sequence number of the global training round,indicating the local convergence accuracy of the t-th round of global training,Lrepresents a Lipschitz constant, gamma represents a strong convexity coefficient, and ξ is a correlation coefficient and satisfies，Which indicates the accuracy of the global convergence,representing the number of rounds of global training.

The constraint that the time of each round of global training is less than the maximum training delay is specifically as follows:

wherein t represents the sequence number of the global training round, N (t) represents the local training round of the t-th global training,which means that the time delay is calculated,which is indicative of the time delay of the communication,the maximum training time delay is indicated by the time,representing the number of rounds of global training.

And (4.2) under the conditions of fixing local convergence precision, calculating frequency allocation and bandwidth allocation, converting the original problem into a shaping variable optimization problem to solve the local optimal solution of the local training round, and greatly reducing the solving difficulty of the original problem, so that the local training round is optimized according to the constraint of the local training round and the constraint that the time of each round of global training is less than the maximum training time delay.

The constraints of the local training rounds involved in the process are specifically as follows:

wherein the content of the first and second substances,Lexpressing the Lipschitz constant, gamma expressing the strong convexity coefficient, lambda expressing the learning rate of the local training, t expressing the serial number of the global training round, N (t) expressing the local training round of the t-th global training,indicating the local convergence accuracy of the t-th round of global training,representing the number of rounds of global training.

The constraint that the time of each round of global training is less than the maximum training delay is specifically as follows:

wherein t represents the sequence number of the global training round, N (t) represents the local training round of the t-th global training,which means that the time delay is calculated,which is indicative of the time delay of the communication,the maximum training time delay is indicated by the time,representing the number of rounds of global training.

(4.3) under the condition of fixing the local convergence precision and the local training round, converting the original problem into a convex optimization problem to solve the local optimal solution of the calculated frequency allocation and the bandwidth allocation, and greatly reducing the solving difficulty of the original problem, so that the frequency allocation and the bandwidth allocation are optimized and calculated by using a CVX solving tool according to the constraint of the total bandwidth, the constraint of the local training round, the local convergence precision, the calculation frequency of the CPU of the unmanned aerial vehicle and the constraint of the value interval of the sub-bandwidth;

the constraints on the total bandwidth involved in this process are specifically:

wherein t represents the sequence number of the global training round,the sub-bandwidth allocated by the k-th frame following the unmanned aerial vehicle in the t-th round of global training is shown, and B is the total bandwidth.

The constraints of the local training rounds are specifically as follows:

wherein the content of the first and second substances,Lexpressing the Lipschitz constant, gamma expressing the strong convexity coefficient, lambda expressing the learning rate of the local training, t expressing the serial number of the global training round, N (t) expressing the local training round of the t-th global training,indicating the local convergence accuracy of the t-th round of global training,representing the number of rounds of global training.

The constraints of the local convergence precision, the unmanned aerial vehicle CPU calculation frequency and the value interval of the sub-bandwidth are specifically as follows:

wherein t represents the sequence number of the global training round,indicating the local training accuracy of the global training at the t-th round,indicating the calculated frequency of the kth frame following the drone in the tth round of global training,representing the maximum calculation frequency of the kth frame following unmanned aerial vehicle, and representing that the kth frame follows the unmanned aerial vehicle in the t-th round global trainingThe sub-bandwidth to which it is allocated,representing the number of rounds of global training.

And (4.4) repeating the iteration steps (4.1) - (4.3) until the relative error of the total training energy consumption of the unmanned aerial vehicle cluster is less than 0.01, obtaining the minimized total training energy consumption of the unmanned aerial vehicle cluster, and obtaining the optimal local convergence precision, local training turn, the CPU frequency of the following unmanned aerial vehicle and the sub bandwidth of the following unmanned aerial vehicle. By carrying out iterative solution on the three sub-problems, the solution difficulty of the optimization problem is greatly reduced under the condition of ensuring the accuracy.

(5) Inputting the optimal local convergence precision, the local training turn, the CPU frequency of the following unmanned aerial vehicle and the sub bandwidth of the following unmanned aerial vehicle into the corresponding following unmanned aerial vehicle, so that the following unmanned aerial vehicle can consume the minimum energy to complete the training of the inspection task learning model, and more battery margins follow the leading unmanned aerial vehicle to perform the inspection task.

Fig. 2 shows a simulation result diagram of total training energy consumption of the unmanned aerial vehicle cluster under different global convergence accuracies when federate learning is performed on a cluster consisting of 5 following unmanned aerial vehicles and 1 leading unmanned aerial vehicle, the method for minimizing total training energy consumption of the unmanned aerial vehicle cluster (JTRO) has good convergence, and when the global convergence accuracy is 0.1, compared with a method for directly performing patrol tasks without optimization, the method for performing patrol tasks by minimizing the total training energy consumption of the unmanned aerial vehicle cluster can reduce the total training energy consumption of the unmanned aerial vehicle cluster by 45.07%. On the other hand, as can be seen from the total unmanned aerial vehicle cluster training energy consumption corresponding to different global convergence accuracies, the required total unmanned aerial vehicle cluster training energy consumption increases with the decrease of the global convergence accuracy value.

Fig. 3 is a diagram of a relationship between total training energy consumption of an unmanned aerial vehicle cluster and total bandwidth under different algorithms, the total bandwidth is sequentially increased by 0.8, 0.9, 1.0, 1.1 and 1.2 mhz, and the total training energy consumption of the unmanned aerial vehicle cluster adopting 5 different algorithm schemes is respectively recorded. As can be seen from fig. 3, as the total bandwidth increases, the total training energy consumption of the drone cluster continuously decreases, and meanwhile, compared with the 4 other comparison algorithms, the method for minimizing the total training energy consumption of the drone cluster of the present invention can reduce the total training energy consumption of the drone cluster by 31.48%, 54.99%, 36.63%, and 26.40%, respectively.

Fig. 4 is a graph of a relationship between total training energy consumption of an unmanned aerial vehicle cluster and maximum global time delay under different algorithms, where the maximum global time delay is sequentially increased from 1 second, 1.2 seconds, 1.4 seconds, 1.6 seconds, and 1.8 seconds, and total training energy consumption of the unmanned aerial vehicle cluster adopting 5 different algorithm schemes is respectively recorded, and it can be seen from fig. 4 that total training energy consumption of the unmanned aerial vehicle cluster adopting JTRO, JOCB-FT, and JOCB-RT algorithms is continuously reduced along with the increase of the maximum global time delay. And the JOTB-FC and the JOTB-RC are kept unchanged, because the maximum global time delay is mainly used for restricting the CPU calculation frequency of the unmanned aerial vehicle, and in the maximum global time delay intervals, the corresponding CPU calculation frequency of the unmanned aerial vehicle reaches the optimum.

Fig. 5 is a graph of a relationship between total training energy consumption of the unmanned aerial vehicle cluster and data volume under different algorithms, where the data volume is sequentially increased by 800, 850, 900, 950, and 1000 samples, and the total training energy consumption of the unmanned aerial vehicle cluster when the number of samples under different data volumes is 800, 850, 900, 950, and 1000 is recorded by considering 5 different algorithm schemes. As can be seen from FIG. 5, the total training energy consumption of the UAV cluster increases with the increase of the data volume, and when the number of samples increases from 800 to 1000, the total training energy consumption of the UAV cluster adopting JTRO, JOTB-FC, JOTB-RC, JOCB-FT and JOCB-RT algorithms increases by 8.72%, 9.35%, 14.52%, 35.72% and 27.67%, respectively.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.