A method for autonomously jamming and intercepting missiles by unmanned aerial vehicles

Abstract

The invention discloses a method for autonomously jamming and intercepting missiles by unmanned aerial vehicles, comprising the following steps: S1, obtaining the ballistic trajectory of the missile through a radar, and calculating the best interception position of each missile according to the ballistic trajectory and the target to be protected, which is called the protection position; S2, initializing the population and setting related parameters based on the improved artificial bee colony algorithm; S3, in the hired bee stage, taking each protection position as a base point, recruiting hired bees based on the minimum distance greedy selection and the maximum distance minimum search and selection strategy; S4, in the follower bee stage, the follower bees select the protection position to be followed according to the yield; S5, in the regular migration stage, based on the bird flock algorithm, and improving the flight route of the unmanned aerial vehicle; S6, in the scout bee stage, if there are hired bees that have not been recruited in step S3, they are transformed into scout bees, and the scout bees generate new nectar sources; S7, judging whether the preset termination condition is met, if so, the process is terminated, otherwise, the process is returned to step S3 for iterative operation.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) air defense and anti-missile, and in particular to a method for autonomously interfering with and intercepting missiles by unmanned aerial vehicles. Background Art

[0002] Driven by scientific and technological progress, precision guidance technology has been continuously upgraded, improved, and replaced. The hit probability, accuracy, and anti-interference capability of missiles are getting higher and higher. In actual combat, it is becoming increasingly difficult for targets without interference and camouflage to survive, which has brought huge challenges to air defense and anti-missile. The main methods for destroying incoming missiles include fragmentation interception technology, firepower salvo suppression, and the "many-on-one" interception method of air defense missiles. The fragmentation interception and firepower salvo suppression interception have limited effective areas, and the probability of successful interception is related to the density of firepower. The enemy can control the missile to avoid the effective area. The "many-on-one" interception method of air defense missiles is not cost-effective. Some air defense missiles are more expensive than incoming missiles. There is a certain probability of successful interception of subsonic missiles, but the successful interception rate of hypersonic and intelligent missiles is not high.

[0003] Based on the requirements of air defense and anti-missile missions, some countries and militaries have turned their attention to drone swarm air interception technology. Using a large number of intelligent drones with autonomous combat capabilities to form combat swarms, saturation strikes and air interception of targets have become an important combat style in future air combat. At present, in the field of drone swarms dealing with precision-guided missiles, the main research is on drone evasion of missiles, while there is no mature method for drone swarms to interfere with precision guidance. Summary of the invention

[0004] In order to solve the problems raised in the above background technology, the technical solution adopted by the present invention is:

[0005] A method for autonomously jamming and intercepting missiles by a drone comprises the following steps:

[0006] S1. Obtain the missile's ballistic trajectory through radar, and calculate the best interception position for each missile based on the ballistic trajectory and the target to be protected, which is called the protection position;

[0007] S2. Based on the improved artificial bee colony algorithm, perform population initialization and related parameter settings. Specifically, regard each drone as a bee, each protected position as a nectar source, set the role of the drone and the behavior corresponding to the role, and set the bee colony parameters. The roles of the drone are divided into hired bees, follower bees and scout bees. The initial role of each drone is hired bees or follower bees. The hired bees, follower bees and scout bees mentioned below all refer to drones with corresponding roles.

[0008] S3, hired bee stage, taking each protection position as the base point, hired bees are recruited based on the distance of the drone relative to the protection position, until the number of hired bees recruited at each protection position reaches the preset maximum number, and the hired bees are scheduled to follow the recruited protection position;

[0009] S4, the following bee stage, the following bee selects the protection position to follow according to the rate of return, and the following bee is scheduled to follow the selected protection position;

[0010] S5, regular migration stage, based on the bird flock algorithm, overall analysis of the preset flight routes of all drones processed by steps S3 and S4, and improvement of the flight routes of the drones;

[0011] S6, scout bee stage, if there are employed bees that have not been recruited in step S3, they will be transformed into scout bees, and the scout bees will produce new nectar sources;

[0012] S7: Determine whether the preset termination condition is met. If so, end the process and determine the optimal flight routes for all drones. Otherwise, return to step S3 and iterate.

[0013] In some embodiments, in step S2, when the population is initialized and related parameters are set, it includes setting the number of drones and obtaining the number of missiles that need to be interfered with and intercepted, where n represents the number of drones, and m represents the number of missiles that need to be interfered with and intercepted, and there are correspondingly m protection positions.

[0014] In some embodiments, step S3 specifically includes the following steps:

[0015] S31. Determine the threat level of each missile based on the positional relationship between the missile trajectory and the target to be protected. The higher the threat level of the missile, the higher the fitness value of the corresponding protection position. The fitness value determines the number of hired bees that need to be recruited for the protection position.

[0016] S32, based on the minimum distance search, the drones are allocated according to the relative distance between each protection position and the n drones, and when the number of drones allocated to any protection position reaches its appropriate value, the search for the protection position is stopped;

[0017] S33, for the drone that is searched by more than two protection positions in step S32, greedily select according to the minimum distance and assign the drone to a specific protection position;

[0018] S34. For the protection positions that are not allocated with enough drones, expand the search range and continue searching. For the newly generated drones that are searched by more than two protection positions, process them in the same way as step S33 until the number of drones allocated to all protection positions reaches its appropriate value.

[0019] In some embodiments, in step S3, after step S34, the following steps are further included:

[0020] S35. For any protection position O j , find its adjacent protection position O i , where i = 1, 2, ..., m; j = 1, 2, ..., m; i ≠ j, select the one assigned to the protection position O j And the distance to the protection position O j The farthest drone x j，max , and select the one assigned to the protection position O i And the distance to the protection position O j Recent drones x i，min ;

[0021] S36, calculate the drone x j，max Arrive at protection position O j The distance D jj and reach the protection position O i The distance D ji , and calculate the drone x i，min Arrive at protection position O j The distance D ij and reach the protection position O i The distance D ii ;

[0022] S37, set the maximum distance D jmax =max(D jj ,D ji ), D imax =max(D ii ,D ij ), assign the two drones to protection positions for crossover and greedy selection. If D jmax ≤D imax , then the protection position selection remains unchanged, if D imax <D jmax , then drone x j，max and x i，min The allocated protection positions are cross-interchanged;

[0023] S38. Repeat the process of steps S35-S37 until the maximum flight distance of all the drones selected as hired bees reaches the minimum state.

[0024] In some embodiments, in step S5, based on the bird flock algorithm, each protection position is regarded as the gathering center of the corresponding drone, so that the drones with closer distances have higher priority to fly to the protection position;

[0025] At the same time, a safety distance is preset. For each drone, obstacles within the safety distance in its flight path are automatically detected, and the flight path of each drone is adjusted as a whole, so that the drones automatically avoid obstacles and do not interfere with each other during flight.

[0026] In some embodiments, in step S7, the preset termination condition includes the number of iterations and the interception degree of each protection position;

[0027] The interception degree is determined by the number of drones recruited as hired bees in the protection position, the interception area and relative distance of each drone, where the interception area is the projection area of ​​the drone on the vertical plane of the missile's flight trajectory, and the relative distance is the vertical distance of the drone relative to the missile's flight trajectory.

[0028] The more employed bees corresponding to each protection position, the larger the interception area, and the smaller the relative distance, the higher the interception degree;

[0029] When the number of iterations reaches the preset value and each protected position meets the preset requirements, the process ends and the optimal flight route for all drones is determined.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] The method for autonomous interference and missile interception by unmanned aerial vehicles provided by the present invention improves the traditional artificial bee colony algorithm and combines the bird colony algorithm in the regular migration stage. It can realize autonomous interference and missile interception by unmanned aerial vehicles, effectively reduce the flight path consumption and deployment time of unmanned aerial vehicles, and the algorithm has good stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A flow chart of the method for autonomously jamming and intercepting missiles by a UAV provided by the present invention;

[0033] Figure 2 is a schematic diagram of a test scenario in a specific embodiment;

[0034] Figure 3a and Figure 3b The performance comparison chart of different algorithms. DETAILED DESCRIPTION

[0035] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the following further describes how the present invention is implemented in conjunction with the accompanying drawings and specific implementation methods.

[0036] Reference Figure 1 As shown, the present invention provides a method for autonomously jamming and intercepting missiles by a UAV, comprising the following steps:

[0037] S1. Obtain the missile's ballistic trajectory through radar, and calculate the best interception position for each missile based on the ballistic trajectory and the target to be protected, which is called the protection position;

[0038] S2. Based on the improved artificial bee colony algorithm, perform population initialization and related parameter settings. Specifically, regard each drone as a bee, each protected position as a nectar source, set the role of the drone and the behavior corresponding to the role, and set the bee colony parameters. The roles of the drone are divided into hired bees, follower bees and scout bees. The initial role of each drone is hired bees or follower bees. The hired bees, follower bees and scout bees mentioned below all refer to drones with corresponding roles.

[0039] S3, hired bee stage, taking each protection position as the base point, hired bees are recruited based on the distance of the drone relative to the protection position, until the number of hired bees recruited at each protection position reaches the preset maximum number, and the hired bees are scheduled to follow the recruited protection position;

[0040] S4, the following bee stage, the following bee selects the protection position to follow according to the rate of return, and the following bee is scheduled to follow the selected protection position;

[0041] S5, regular migration stage, based on the bird flock algorithm, overall analysis of the preset flight routes of all drones processed by steps S3 and S4, and improvement of the flight routes of the drones;

[0042] S6, scout bee stage, if there are employed bees that have not been recruited in step S3, they will be transformed into scout bees, and the scout bees will produce new nectar sources;

[0043] S7: Determine whether the preset termination condition is met. If so, end the process and determine the optimal flight routes for all drones. Otherwise, return to step S3 and iterate.

[0044] Furthermore, in step S2, when the population is initialized and related parameters are set, including the setting of the number of drones and the acquisition of the number of missiles that need to be interfered with and intercepted, n represents the number of drones, m represents the number of missiles that need to be interfered with and intercepted, and there are correspondingly m protection positions.

[0045] Bees are social insects. The colony consists of three forms: worker bees, drones, and queen bees. The behavior of a single bee is simple, including collecting nectar, reproduction, feeding, guarding the hive, and protecting the queen bee. The actions of the group are composed of multiple simple individual behaviors. Through clear division of labor and solidarity and cooperation, the reproduction and survival of the bee colony are jointly maintained. The bee colony can quickly adapt to changes in the environment and collect nectar from food sources with extremely high efficiency. The Artificial Bee Colony (ABC) algorithm simulates the division of labor and cooperation of the bee colony to collect nectar. It realizes the process of nectar source reconnaissance and collection through the worker bees' exploration of the value of the nectar source, the reconnaissance of location information, the exchange of information between the bee colonies, and the individual behavior decision-making of the bees.

[0046] The ABC algorithm is used to interfere with and intercept incoming missiles. The relative distance between the UAV and the protection position is used as the minimum search model. The overall pattern is to recruit UAVs one by one, which is disordered and easy to fall into the local optimal solution. Further optimization is needed to solve the problem of applying multiple UAVs to multi-target allocation.

[0047] Preferably, step S3 specifically includes the following steps:

[0048] S31. Determine the threat level of each missile based on the positional relationship between the missile trajectory and the target to be protected. The higher the threat level of the missile, the higher the fitness value of the corresponding protection position. The fitness value determines the number of hired bees that need to be recruited for the protection position.

[0049] S32, based on the minimum distance search, the drones are allocated according to the relative distance between each protection position and the n drones. The closer the distance, the easier it is to be selected. When the number of drones selected by any protection position reaches its protection level requirement, the search for the protection position is stopped;

[0050] S33, for the drones selected by more than two protection positions in step S32, greedy selection is performed according to the minimum distance, and the smaller the distance to the protection position, the easier it is to be selected;

[0051] S34. For the protection positions that are not allocated with enough drones, expand the search range and continue searching. For the newly generated drones that are searched by more than two protection positions, process them in the same way as step S33 until the number of drones allocated to all protection positions reaches its appropriate value.

[0052] Furthermore, after step S34, the following steps are also included:

[0053] S35. For any protection position O j , find its adjacent protection position O i, where i = 1, 2, ..., m; j = 1, 2, ..., m; i ≠ j, select the one assigned to the protection position O j And the distance to the protection position O j The farthest drone x j，max , and select the one assigned to the protection position O i And the distance to the protection position O j Recent drones x i，min ;

[0054] S36, calculate the drone x j，max Arrive at protection position O j The distance D jj and reach the protection position O i The distance D ji , and calculate the drone x i，min Arrive at protection position O j The distance D ij and reach the protection position O i The distance D ii ;

[0055] S37, set the maximum distance D jmax =max(D jj ,D ji ), D imax =max(D ii ,D ij ), assign the two drones to protection positions for crossover and greedy selection. If D jmax ≤D imax , then the protection position selection remains unchanged, if D imax <D jmax , then drone x j，max and x i，min The allocated protection positions are cross-interchanged;

[0056] S38. Repeat the process of steps S35-S37 until the maximum flight distance of all the drones selected as hired bees reaches the minimum state.

[0057] Through the above steps S31-S38, the maximum flight distance can be minimized, and a better result is obtained overall, which can improve the problem that the traditional artificial bee colony algorithm is prone to fall into the local optimal solution.

[0058] Bird Swarm Algorithm (BSA) is a biologically inspired algorithm proposed based on the foraging, vigilance and migration behaviors of bird flocks in nature. The main features of this algorithm are decentralized search, maintaining population diversity and avoiding falling into local optimal solutions.

[0059] Preferably, in step S5, based on the bird flock algorithm, each protection position is regarded as the gathering center of the corresponding drone, so that the drones with closer distances have higher priority to fly to the protection position;

[0060] At the same time, a safety distance is preset. For each drone, obstacles within the safety distance in its flight path are automatically detected, and the flight path of each drone is adjusted as a whole so that the drones automatically avoid obstacles and do not interfere with each other during flight. The optimization is performed again according to steps S31-S38 to further optimize the time to complete the mission deployment.

[0061] The present invention combines the bird flock algorithm with the improved artificial bee colony algorithm to avoid falling into the local optimal solution and achieve overall optimization. The algorithm has strong stability, and the flight of the UAV can be kept continuous to avoid confusion and realize automatic obstacle avoidance.

[0062] Furthermore, in step S7, the preset termination conditions include the number of iterations and the interception degree of each protection position; the interception degree is determined by the number of drones with the role of hired bees recruited by the protection position, the interception area and relative distance of each drone, wherein the interception area is the projection area of ​​the drone on the vertical plane of the missile flight trajectory, and the relative distance is the vertical distance of the drone relative to the missile flight trajectory; the more hired bees corresponding to each protection position, the larger the interception area, and the smaller the relative distance, the higher the interception degree; when the number of iterations reaches the preset value and each protection position meets the preset requirements, the process is terminated to determine the optimal flight routes of all drones.

[0063] The present invention evaluates the interference interception effect by defining the interception degree, which can effectively ensure the final interference interception effect. Of course, the termination condition preset in step S7 is not limited to the above-mentioned number of iterations and interception degree. Different evaluation criteria can be flexibly set according to actual conditions, as long as the final interference interception effect of the drone can be guaranteed.

[0064] In a specific embodiment, in order to verify the effect of the method of autonomous interference and missile interception by unmanned aerial vehicles provided by the present invention, a simulation test is conducted. The algorithm provided by the present invention is hereinafter referred to as the Self-protection Artificial Bee Colony (SPABC) algorithm, and is compared with the scenarios using the aforementioned traditional Artificial Bee Colony (ABC) algorithm and Bird Swarm Algorithm (BSA).

[0065] Reference Figure 2As shown in the figure, in a 100km×100km battlefield environment, there is a key target facing the enemy's precision-guided fire attack. Reconnaissance finds that 4 missiles will attack the target. The radar detection accuracy is 0.3°, the radar warning distance is 300km, the interception protection position is 500m away from the target, and the missile flight speed is 20 Mach. There are 36 drones carrying out the alert protection mission for the key target. The drone codes are from 1 to 36. The maximum flight speed of each drone is 20m / s. The protection area after deployment is 4m 2 , deployment time 2s, safe distance in flight 1m, safe distance in hovering 0.3m. For ease of calculation, the simulation is set at a horizontal plane with a height of 30m, and the key target coordinates are (2000, 2000). Two groups of tests are carried out respectively.

[0066] In the first group of experiments, in normal state, 36 UAVs were deployed in a 6×6 formation according to the coding sequence, among which the No. 1 UAV was deployed at the coordinates of (2000, 2000), and the other UAVs were deployed at intervals of 2m. The coordinates of No. 36 UAV were (2010, 1990), simulating 4 enemy missiles to carry out precision-guided strikes on important targets. The interference interception positions calculated by the system were processed by simulation in the SPABC algorithm, ABC algorithm and BSA algorithm respectively. The average flight distance, maximum distance and deployment completion time of the UAVs after simulation operation of the three algorithms were compared, and the simulation results are shown in Table 1.

[0067] Table 1

[0068]

[0069] It can be concluded from Table 1 that the three algorithms can effectively allocate an appropriate number of drones to implement interference interception on missiles, verifying the feasibility of the proposed algorithm. Table 1 reflects the performance of the three algorithms when the best interference interception effect is tested when the drones are relatively concentrated. It can be seen from the table that the overall performance of the three algorithms is relatively close. From the perspective of average flight path and completion deployment time, the SPABC algorithm has the smallest path consumption and the shortest deployment time.

[0070] In the second set of experiments, in a wartime state, 36 drones performed a round of interference and interception missions and then maintained a patrol state in the battlefield space after simulating a round of attacks. The simulation results are shown in Table 2.

[0071] Table 2

[0072]

[0073] It can be seen that when the drone swarm is in the two states of centralized deployment and random deployment, when the protection value is set to the highest, the deployment time and average flight path of the SPABC algorithm are significantly shorter than those of the ABC algorithm and the BSA algorithm. The average flight path of the ABC algorithm is smaller than that of the BSA algorithm, but the deployment time is significantly longer than that of the BSA algorithm, indicating that the flight path of individual drones in the ABC algorithm is too large, resulting in local solution overflow.

[0074] Refer to Figure 3a and Figure 3b As shown in the figure, it reflects the relationship between the interference interception degree and the average flight path and deployment time of the drone swarm. It can be seen from the figure that the higher the interception degree, the more drones are required and the longer the deployment time. When the interception degree exceeds 0.78, the deployment time of the ABC algorithm increases significantly. There are cases where the flight path of some drones is too large, which shows that the ABC algorithm is prone to fall into the local optimal solution. Among the three algorithms, the deployment time of the SPABC algorithm increases steadily with the increase of the interception degree. The deployment time and the average flight path are always in the optimal state among the three algorithms, which verifies that the SPABC algorithm has the best overall performance.

[0075] At present, precision-guided and precise strikes have become an effective means of preemptive attack on the enemy in modern warfare, running through all links in the war. It is of great significance to introduce drone swarms into terminal air defense and anti-missile by integrating the diversified combat performance of drone swarms. The present invention studies the problem of drone swarms interfering with missiles, forms an effective interference area and interception array through drone swarms, and at the same time, for the high-speed flight speed of missiles, uses the maneuverability of drones to improve the interference interception time, and establishes an effective interference interception combat model. In view of the problem that the ABC algorithm is prone to fall into the local optimal solution, the ABC algorithm and the BSA algorithm are introduced into the problem of interfering with missiles, and the SPABC algorithm is proposed to strengthen the overall search and optimization capabilities of drone swarms. The simulation results show that the introduction of the SPABC algorithm into the task of drone swarms interfering with missiles is an effective algorithm, which has good performance in overall optimization and stability, can reduce the flight path consumption and deployment time of drones, and provides a new solution for applying drone swarms to terminal interference interception missiles.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for autonomously jamming and intercepting missiles by a drone, characterized in that: The steps include: S1. Obtain the missile's ballistic trajectory through radar, and calculate the best interception position for each missile based on the ballistic trajectory and the target to be protected, which is called the protection position; S2. Based on the improved artificial bee colony algorithm, perform population initialization and related parameter settings. Specifically, regard each drone as a bee, each protected position as a nectar source, set the role of the drone and the behavior corresponding to the role, and set the bee colony parameters. The roles of the drone are divided into hired bees, follower bees and scout bees. The initial role of each drone is hired bees or follower bees. The hired bees, follower bees and scout bees mentioned below all refer to drones with corresponding roles. S3, hired bee stage, taking each protection position as the base point, hired bees are recruited based on the distance of the drone relative to the protection position, until the number of hired bees recruited at each protection position reaches the preset maximum number, and the hired bees are scheduled to follow the recruited protection position; S4, the following bee stage, the following bee selects the protection position to follow according to the rate of return, and the following bee is scheduled to follow the selected protection position; S5, regular migration stage, based on the bird flock algorithm, overall analysis of the preset flight routes of all drones processed by steps S3 and S4, and improvement of the flight routes of the drones; S6, scout bee stage, if there are employed bees that have not been recruited in step S3, they will be transformed into scout bees, and the scout bees will produce new nectar sources; S7, determine whether the preset termination condition is met, if so, end the process and determine the optimal flight path for all drones, otherwise return to step S3 for iterative operation; Among them, in step S2, when the population is initialized and related parameters are set, including the setting of the number of drones and the acquisition of the number of missiles that need to be interfered with and intercepted, n represents the number of drones, m represents the number of missiles that need to be interfered with and intercepted, and there are m protection positions accordingly; Step S3 specifically includes the following steps: S31. Determine the threat level of each missile based on the positional relationship between the missile trajectory and the target to be protected. The higher the threat level of the missile, the higher the fitness value of the corresponding protection position. The fitness value determines the number of hired bees that need to be recruited for the protection position. S32, based on the minimum distance search, the drones are allocated according to the relative distance between each protection position and the n drones, and when the number of drones allocated to any protection position reaches its appropriate value, the search for the protection position is stopped; S33, for the drone that is searched by more than two protection positions in step S32, greedily select according to the minimum distance and assign the drone to a specific protection position; S34, for the protection positions that are not assigned enough drones, expand the search range and continue searching. For the newly generated drones that are searched by more than two protection positions, process them in the same way as step S33 until the number of drones assigned to all protection positions reaches its appropriate value; After step S34, the following steps are also included: S35. For any protection position O j , find its adjacent protection position O i , where i = 1, 2, ..., m; j = 1, 2, ..., m; i ≠ j, select the one assigned to the protection position O j And the distance to the protection position O j The farthest drone x j，max , and select the one assigned to the protection position O i And the distance to the protection position O j Recent drones x i，min ; S36, calculate the drone x j，max Arrive at protection position O j The distance D jj and reach the protection position O i The distance D ji , and calculate the drone x i，min Arrive at protection position O j The distance D ij and reach the protection position O i The distance D ii ; S37, set the maximum distance D jmax =max(D jj ,D ji ), D imax =max(D ii ,D ij ), assign the two drones to protection positions for crossover and greedy selection. If D jmax ≤D imax , then the protection position selection remains unchanged, if D imax <D jmax , then drone x j，max and x i，min The allocated protection positions are cross-interchanged; S38. Repeat the process of steps S35-S37 until the maximum flight distance of all the drones selected as hired bees reaches the minimum state.

2. The method for autonomously jamming and intercepting missiles by unmanned aerial vehicles according to claim 1 is characterized in that: In step S5, based on the bird flock algorithm, each protection position is regarded as the gathering center of the corresponding drone, so that the drones with closer distances have higher priority to fly to the protection position; At the same time, a safety distance is preset. For each drone, obstacles within the safety distance in its flight path are automatically detected, and the flight path of each drone is adjusted as a whole, so that the drones automatically avoid obstacles and do not interfere with each other during flight.

3. The method for autonomously jamming and intercepting missiles by unmanned aerial vehicles according to claim 2 is characterized in that: In step S7, the preset termination conditions include the number of iterations and the interception degree of each protection position; The interception degree is determined by the number of drones recruited as hired bees in the protection position, the interception area and relative distance of each drone, where the interception area is the projection area of ​​the drone on the vertical plane of the missile's flight trajectory, and the relative distance is the vertical distance of the drone relative to the missile's flight trajectory. The more employed bees corresponding to each protection position, the larger the interception area, and the smaller the relative distance, the higher the interception degree; When the number of iterations reaches the preset value and each protected position meets the preset requirements, the process ends and the optimal flight route for all drones is determined.

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