Method and device for planning interference of unmanned aerial vehicle group, electronic equipment and storage medium

Abstract

The application provides a method and device for planning drone swarm interference, electronic equipment and storage medium. The method comprises the following steps: constructing an interference planning objective function based on the path length and interference benefit of the drone swarm, and constructing an interference constraint condition based on a preset drone interference rule; determining an interference planning population of the drone swarm including at least one interference planning result based on the interference constraint condition and a target to be interfered; the interference planning result includes an interference path and an interference duration of at least one drone; determining a target interference planning result based on the interference planning population, a population expansion rule and a population selection rule; the population selection rule includes a rule for selecting based on an interference function value of each interference planning result calculated by using the interference planning objective function. According to the scheme, the interference path and the interference duration of the drone swarm are planned at the same time, the optimization of the drone path and the interference duration is realized, and the interference cost of the drone is reduced.

Description

Technical Field

[0001] This application relates to the field of path planning technology, and in particular to a planning method, apparatus, electronic device and storage medium for unmanned aerial vehicle (UAV) swarm interference. Background Technology

[0002] A jammer is an electronic device that transmits or relays electronic jamming signals to disrupt or deceive other electronic devices, reducing their effectiveness or even causing them to malfunction. When jamming missions need to be carried out against multiple target points, a swarm of drones carrying jammers can be used to fly around each target point, allowing the jammers to perform the jamming mission. To achieve jamming of all target points by the drone swarm, the jamming path of the drone swarm needs to be planned. Current technologies can only determine the optimal path of the drones through path planning, but cannot plan the jamming duration for each target point, resulting in high jamming costs when drone swarms perform jamming missions. Summary of the Invention

[0003] Based on the above requirements, this application proposes a planning method, device, electronic equipment, and storage medium for UAV swarm interference, which can plan the UAV interference path and interference duration, optimize the UAV path and interference duration, and reduce the interference cost of UAVs.

[0004] To achieve the above objectives, this application proposes the following technical solution:

[0005] According to a first aspect of the embodiments of this application, a method for planning interference with a drone swarm is provided, comprising:

[0006] Based on the path length and interference benefit of the drone swarm, an interference planning objective function for the drone swarm is constructed, and based on preset drone interference rules, interference constraints for the drone swarm are constructed.

[0007] Based on the interference constraints and the target to be interfered with, an interference planning population of the UAV swarm is determined; the interference planning population includes interference planning results of at least one UAV swarm, and the interference planning results include the interference path and interference duration of at least one UAV.

[0008] Based on the interference planning population, and the pre-set population expansion rules and population selection rules, the target interference planning result is determined; the population selection rules include rules for selecting interference planning results based on the interference function values ​​of each interference planning result, wherein the interference function values ​​of the interference planning results are calculated based on the interference planning objective function.

[0009] Optionally, based on the interference planning population and pre-set population expansion and selection rules, the target interference planning result is determined, including:

[0010] Based on the interference planning population, as well as the pre-set population expansion rules and population selection rules, the interference planning population is iteratively updated, and the interference planning population that reaches the preset iteration end condition is taken as the target interference planning population.

[0011] Using the population selection rules, target interference planning results are selected from the target interference planning population.

[0012] Optionally, based on the interference planning population and pre-set population expansion and selection rules, the interference planning population is iteratively updated, and the interference planning population that reaches the preset iteration termination condition is taken as the target interference planning population, including:

[0013] Based on the pre-set population expansion rules, the interference planning results in the interference planning population are adjusted to obtain the subpopulation corresponding to the interference planning population. The interference planning population and the subpopulation corresponding to the interference planning population are then merged to obtain the merged population.

[0014] Based on the pre-set population selection rules, a preset number of interference planning results are selected from the merged population as the interference planning population after iterative update, and it is determined whether the current iteration conditions have reached the preset iteration end conditions.

[0015] If the current iteration conditions meet the preset iteration end conditions, then the iteratively updated interference planning population will be used as the target interference planning population.

[0016] If the current iteration conditions do not meet the preset iteration end conditions, then based on the population expansion rules, the interference planning results in the iteratively updated interference planning population are adjusted to determine the updated merged population. Based on the preset population selection rules, a preset number of interference planning results are selected from the updated merged population as the iteratively updated interference planning population.

[0017] Optionally, based on a pre-set population selection rule, a preset number of interference planning results are selected from the merged population as the iteratively updated interference planning population, including:

[0018] Using the aforementioned interference planning objective function, the interference function value of each interference planning result in the merged population is calculated;

[0019] Based on the interference function values ​​of each interference planning result, the non-dominated order of each interference planning result is determined, and the crowding degree of each interference planning result is calculated; the crowding degree of the interference planning result represents the density of the interference planning result in the merged population.

[0020] Based on the non-dominated order and crowding degree of each interference planning result, a preset number of interference planning results are selected from the merged population as the iteratively updated interference planning population.

[0021] Optionally, based on the interference function values ​​of each interference planning result, the non-dominated order of each interference planning result is determined, including:

[0022] Based on the interference function values ​​of each interference planning result in the merged population, the number of dominated interference planning results is determined; the number of dominated interference planning results represents the number of other interference planning results that dominate the interference planning result in the merged population; the first interference planning result dominating the second interference planning result means that the interference function value of the first interference planning result is better than the interference function value of the second interference planning result.

[0023] Based on all currently determined non-dominated orders, determine the non-dominated order of the disturbance planning results with a dominated number of 0;

[0024] The interference planning results with determined non-dominated orders are removed from the merged population. The number of dominated results of the remaining interference planning results is determined based on the interference function value of the remaining interference planning results in the merged population. The non-dominated order of the interference planning results with a number of dominated 0 in the remaining interference planning results is determined based on all the currently determined non-dominated orders, until all interference planning results in the merged population are removed.

[0025] Optionally, the interference planning objective function includes: an interference path planning function and an interference duration planning function, and the interference function value includes: an interference path function value and an interference duration function value;

[0026] Based on the interference function values ​​of each interference planning result, the congestion degree of the interference planning result is calculated, including:

[0027] Calculate the first difference between the interference path function values ​​and the second difference between the interference duration function values ​​of two interference planning results adjacent to the interference planning result;

[0028] Based on the interference path function value and interference duration function value of all interference planning results in the merged population, determine the third difference between the maximum interference path function value and the minimum interference path function value, and the fourth difference between the maximum interference duration function value and the minimum interference duration function value.

[0029] The ratio between the first difference and the third difference is taken as the first congestion degree of the interference planning result on the interference path planning function, the ratio between the second difference and the fourth difference is taken as the second congestion degree of the interference planning result on the interference duration planning function, and the sum of the first congestion degree and the second congestion degree is taken as the congestion degree of the interference planning result.

[0030] Optionally, using the population selection rule, the target interference planning result is selected from the target interference planning population, including:

[0031] Based on the interference function values ​​of each interference planning result in the target interference planning population, the non-dominated order of each interference planning result in the target interference planning population is determined by performing a non-dominated sort.

[0032] The interference planning result with the highest non-dominant order in the target interference planning population is taken as the target interference planning result.

[0033] Optionally, based on the interference constraints and the target to be interfered with, the interference planning population of the UAV swarm is determined, including:

[0034] All targets to be interfered with in a pre-set interference task are randomly arranged to form at least one target sequence;

[0035] According to the interference constraints and the order of each target to be interfered in the target sequence, the drones in the drone swarm are assigned targets to be interfered and the interference duration of each target to be interfered is divided to obtain the interference planning result corresponding to the target sequence.

[0036] At least one interference planning result is used as the interference planning population of the UAV swarm.

[0037] Optionally, the interference planning results are represented using pre-set chromosome encoding rules;

[0038] The interference planning results include path chromosome segments and duration chromosome segments;

[0039] The path chromosome segment contains at least one interference path of a UAV, and the interference path of the UAV includes: the serial numbers of the targets to be interfered with arranged in the interference order of each target to be interfered with by the UAV.

[0040] The duration chromosome segment contains interference duration labels for the targets to be interfered with, arranged in the order of the targets to be interfered with in the path chromosome segment.

[0041] According to a second aspect of the embodiments of this application, a planning device for unmanned aerial vehicle (UAV) swarm interference is provided, comprising:

[0042] The module is used to construct the interference planning objective function of the UAV swarm based on the path length and interference benefit of the UAV swarm, and to construct the interference constraints of the UAV swarm based on preset UAV interference rules.

[0043] The population determination module is used to determine the interference planning population of the UAV swarm based on the interference constraints and the target to be interfered with; the interference planning population includes the interference planning results of at least one UAV swarm, and the interference planning results include the interference path and interference duration of at least one UAV.

[0044] The planning result determination module is used to determine the target interference planning result based on the interference planning population and the pre-set population expansion rules and population selection rules; the population selection rules include rules for selecting interference planning results based on the interference function values ​​of each interference planning result, and the interference function values ​​of the interference planning results are calculated based on the interference planning objective function.

[0045] According to a third aspect of the embodiments of this application, an electronic device is provided, including: a memory and a processor;

[0046] The memory is connected to the processor and is used to store programs;

[0047] The processor is used to implement the above-mentioned planning method for drone swarm interference by running the program in the memory.

[0048] According to a fourth aspect of the embodiments of this application, a storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, it implements the above-described planning method for unmanned aerial vehicle (UAV) swarm interference.

[0049] The proposed method for planning UAV swarm interference involves constructing an interference planning objective function based on the path length and interference benefits of the UAV swarm, and establishing interference constraints based on pre-defined UAV interference rules. Based on these constraints and the target to be interfered with, an interference planning population of the UAV swarm is determined. This population includes interference planning results for at least one UAV swarm, with each result containing the interference path and duration of at least one UAV. Based on the interference planning population, and pre-defined population expansion and selection rules, a target interference planning result is determined. The population selection rules include rules based on the interference function values ​​of each interference planning result calculated using the interference planning objective function. This technical solution allows for the planning of both the path length and interference benefits of the UAV swarm as objectives, while simultaneously planning the interference path and duration, thereby optimizing the UAV path and interference duration and reducing UAV interference costs. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0051] Figure 1 A flowchart illustrating a method for planning interference by a drone swarm, provided in an embodiment of this application;

[0052] Figure 2 A schematic diagram illustrating the process of determining the target interference planning population provided in an embodiment of this application;

[0053] Figure 3 This is a schematic diagram illustrating the iterative update of the interference planning population provided in an embodiment of this application;

[0054] Figure 4 A schematic diagram illustrating the processing flow of non-dominated sorting of interference planning results provided in an embodiment of this application;

[0055] Figure 5 A schematic diagram of a planning device for unmanned aerial vehicle (UAV) swarm interference provided in an embodiment of this application;

[0056] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0057] The technical solution of this application is applicable to drone jamming application scenarios, specifically in application scenarios where drones are planned to perform jamming tasks. By adopting the technical solution of this application, the path length and jamming benefit of the drone swarm can be used as planning objectives, while simultaneously planning the jamming path and jamming duration of the drone swarm, thereby optimizing the drone path and jamming duration and reducing the jamming cost of drones.

[0058] Jammers can disrupt or deceive other electronic devices by transmitting or relaying electronic jamming signals, thereby achieving the function of interfering with electronic equipment. Unmanned aerial vehicles (UAVs) carrying jammers fly around the target to interfere with it. When multiple targets need to be jammed, multiple UAVs can be used, each jamming at least one target. To reduce the jamming cost when UAV swarms perform jamming missions, jamming planning of the UAV swarm is necessary to determine the optimal jamming plan.

[0059] In existing technologies, to control the operating costs of drone swarms, the drones' operating paths are typically planned to determine the optimal path for each drone. However, in the field of drone jamming missions, simply planning jamming paths for each drone against multiple targets and determining the optimal path for each drone can only reduce jamming costs to a certain extent. Furthermore, when drones perform jamming missions, they need to set appropriate jamming durations. Different jamming durations yield different jamming benefits, and longer jamming durations result in higher jamming costs. Therefore, simply planning the drones' jamming paths still leads to high jamming costs.

[0060] Based on this, this application proposes a planning method for UAV swarm interference. This technical solution can use the path length and interference benefits of the UAV swarm as planning objectives, and simultaneously plan the interference path and interference duration of the UAV swarm, thereby solving the problem of high interference costs when UAVs perform interference tasks in the prior art.

[0061] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0062] Exemplary methods

[0063] See Figure 1 As shown in the embodiment of this application, a planning method for unmanned aerial vehicle (UAV) swarm interference is proposed. The method includes:

[0064] S101. Based on the path length and interference benefit of the UAV swarm, construct the interference planning objective function of the UAV swarm, and based on the preset UAV interference rules, construct the interference constraints of the UAV swarm.

[0065] Specifically, when using drones to perform jamming missions against targets, a drone swarm needs to carry jammers from an airport. The drones fly sequentially along their jamming paths to each target, and perform jamming around the target according to the specified jamming duration. This continues until all targets to be jammed by the drone have been jammed, at which point the drone returns to the airport, thus achieving the drone swarm's jamming mission. Therefore, it is evident that the jamming paths of each drone and the duration of jamming at each target are crucial for the execution of the jamming mission. Thus, based on the goal of successfully jamming all targets, it is necessary to plan the jamming paths of each drone and the optimal jamming duration for each target within the drone swarm. This will reduce the path length of the drone swarm, increase the jamming benefit, and ultimately minimize the path length while maximizing the jamming benefit, thereby reducing the overall jamming cost of the drone swarm.

[0066] This embodiment abstracts the interference planning problem of a drone swarm into a mathematical model, which includes the interference planning objective function and the interference constraints of the drone swarm. The interference planning objective of the drone swarm is to minimize the path length and maximize the interference benefit of the drone swarm. Therefore, this embodiment needs to construct the interference planning objective function of the drone swarm based on the path length and interference benefit. The formula for the interference planning objective function of the drone swarm in this embodiment is as follows:

[0067]

[0068] Where f(x) represents the interference planning objective function of the UAV swarm, V_minf(x) represents minimizing the interference planning objective function, K represents the UAV swarm carrying out the interference mission, V represents the set of all targets to be interfered with and the UAV starting points (i.e., airports), and d ij t represents the distance from target i to target j to be interfered with. ik p represents the duration for which drone k interferes with target i. i Let t represent the single-time interference gain of the target i to be interfered with. ik p i x represents the interference benefit of drone k when interfering with target i. ijk This indicates whether the drone k flew from the target i to the target j to be interfered with, i.e.

[0069]

[0070] ∑ k∈K ∑ i∈V ∑ j∈Vx ijk d ij This represents the total path length of the drone swarm, where all drones complete their jamming missions against all targets. k∈K ∑ i∈V t ik p i This represents the interference gain of all UAVs completing the interference tasks on all targets to be interfered with, i.e., the interference gain of the UAV swarm. Since the interference planning objective is to minimize the path length and maximize the interference gain, and the above interference planning objective function is expressed as a function of minimizing the value, 1 / interference gain is used in the function to transform maximizing the interference gain into minimizing 1 / interference gain.

[0071] During the execution of jamming missions, drone swarms must adhere to preset drone jamming rules. Therefore, jamming planning must be constrained by these rules. This embodiment requires constructing jamming constraints for the drone swarm based on these preset rules. The preset drone jamming rules in this embodiment may include the following:

[0072] (1) There is only one airport for drone takeoff, and all drones take off from the airport and return to the airport after the interference is completed;

[0073] (2) The types of drones are limited;

[0074] (3) The speed of the drone is constant at v;

[0075] (4) Each drone in the drone swarm has the same flight range;

[0076] (5) Each target to be interfered with has its own time window for executing the interference task (the time window reflects the earliest time to interfere with the target and the latest time to interfere with the target, etc.);

[0077] (6) When the UAV interferes with the target, it flies around the target at a constant speed v.

[0078] (7) Each UAV can perform jamming tasks against multiple targets, and only one UAV is assigned to each target to be jammed.

[0079] (8) The coordinates of the airport and the target to be interfered with are known;

[0080] (9) Each UAV carrying a jammer executes a mission route (i.e., jamming path);

[0081] (10) The time window for the interference task of each target to be interfered with is constant and known;

[0082] (11) The unit time interference benefit of each target to be interfered with is constant and known;

[0083] (12) Assume that the drone departs from the airport at time 0;

[0084] (13) The impact of adverse weather conditions, aircraft malfunctions and other factors during the execution of the mission is not taken into account.

[0085] Based on the aforementioned preset drone interference rules, the interference constraints for the constructed drone swarm include the following formulas 1-10:

[0086] Formula 1: ∑ k∈K ∑ i∈N x oik -∑ k∈K ∑ j∈N x jok =0

[0087] Formula 1 is used to constrain the starting point o (i.e., the airport) to be both the starting and ending point of UAV k. Here, K represents the swarm of UAVs carrying out the jamming mission, N represents the set of targets to be jammed, and x... oik Indicates whether the drone k flies from the starting point o to the target i to be interfered with, x jok This indicates whether the drone k flew from the target j to the starting point o.

[0088] Formula 2:

[0089] Formula 2 is used to constrain the synchronization between the flight of UAV k from target i to target j or from target j to target i and the execution of the interference task by UAV k on target i. That is, whether UAV k flies from target i to target j or from target j to target i, it will achieve interference with target i. It also constrains target i to be interfered with only once (i.e., interfered with by UAV k only once). ijk Indicates whether UAV k flies from target i to target j, x ijk This indicates whether drone k flew from target j to target i, and whether the jamming task for target i was completed by drone k.

[0090]

[0091] Formula 3:

[0092] Formula 3 is used to constrain the number of drones departing from the airport to not exceed the total number of drones in the drone swarm. ∑ i∈N x oikThis indicates the number of drones that departed from the starting point o (airport).

[0093] Formula 4:

[0094] Formula 4 is used to constrain the number of movements between all targets to be interfered with contained in the interference path of all UAVs to not exceed the total number of targets to be interfered with.

[0095] Formula 5:

[0096] Formula 5 is used to constrain the flight range of each UAV. ik This represents the time it takes for drone k to arrive at the target i to be interfered with. Let v represent the time it takes for drone k to leave target i to be interfered with, v represent the drone's flight speed, and S represent the drone's maximum range. Let ∑ represent the range of the UAV k as it performs the jamming mission around the target i. i∈V ∑ i≠j,j∈V x ijk d ij This represents the distance traveled by the UAV k between the targets to be interfered with along the interference path. This represents the flight distance of UAV k as it flies around each target to be interfered with in the interference path to perform the interference mission. This represents the total distance traveled by UAV k to complete all interference tasks along the interference path (i.e., the total distance from starting point o to returning to starting point o after completing the interference tasks).

[0097] Formula 6:

[0098] Formula 6 represents the time coordination constraints for each UAV. ik t represents the time it takes for drone k to arrive at the target i to be interfered with. ik d represents the time during which drone k interferes with target i. ij / v represents the time it takes for drone k to fly from target i to target j, s jk This represents the time it takes for drone k to arrive at the target j to be interfered with, where M represents a very large positive integer used to adjust the constraints. Indicates when When the value is 1 (i.e., the drone k flies from the target i to the target j), the constraint of Formula 6 takes effect; otherwise, the constraint of Formula 6 does not take effect.

[0099] Formula 7:

[0100] Formula 7 represents the constraint on the interference duration of each UAV for each target to be interfered with. M represents a maximum positive integer, when y kiWhen the value is 1 (i.e., the drone k performs the interference task on the target i to be interfered with), the constraint of Formula 7 takes effect; otherwise, the constraint of Formula 7 does not take effect.

[0101] Formula 8:

[0102] Formula 8 represents the time window constraint for UAV k to interfere with each target. ik e represents the time it takes for drone k to arrive at the target i to be interfered with. i Indicates the earliest time when interference needs to be applied to target i, l i This indicates the latest time when interference needs to be implemented on target i.

[0103]

[0104] Formula 9 represents the minimum interference duration constraint for each target to be interfered with. ∑ k∈K t ik The duration of interference applied to target i is m. i This represents the minimum interference duration for target i.

[0105] Formula 10:

[0106] Formula 10 is for x ijk and y ki Variable constraints.

[0107] S102. Based on the interference constraints and the target to be interfered with, determine the interference planning population of the UAV swarm.

[0108] This embodiment predetermines the interference tasks for the UAV swarm, i.e., all targets to be interfered with, and the location information of each target has been pre-acquired. Based on the interference constraints constructed in the above steps and all targets to be interfered with, and assuming that the interference tasks can be completed for all targets, the UAVs in the swarm are randomly assigned interference tasks and the interference duration of the targets is randomly determined, resulting in the allocation of UAVs. This yields an interference planning result that meets the interference constraints, including the interference path and interference duration of at least one UAV. Following this interference planning result, the interference tasks for all targets to be interfered with can be achieved. This embodiment can obtain multiple interference planning results for the UAV swarm through multiple random assignments, and then combine all the interference planning results of the UAV swarm into a set as the interference planning population of the UAV swarm. That is, this interference planning population includes the interference planning results of at least one UAV swarm, thus achieving the initialization of the interference planning population.

[0109] In addition, this embodiment can also randomly adjust the interference planning results determined by random partitioning according to the interference constraints, so as to obtain new interference planning results that meet the interference constraints, and also incorporate the new interference planning results into the interference planning population.

[0110] As an optional implementation, based on the interference constraints and the target to be interfered with, the interference planning population of the UAV swarm is determined, specifically including the following steps:

[0111] First, all targets to be interfered with in the pre-set interference task are randomly arranged to form at least one target sequence.

[0112] This embodiment randomly arranges all targets to be interfered with in a pre-set interference task of a drone swarm, thereby obtaining a randomly arranged target sequence, which includes all targets to be interfered with. Multiple random arrangements of all targets to be interfered with can be performed, resulting in various different target sequences. Therefore, this embodiment can obtain at least one target sequence. For example, the target sequence can be represented as Sep: [j, j+1, ..., n, 1, ..., j-1], and each target to be interfered with in the target sequence is numbered according to the order of arrangement. That is, the first target j to be interfered with is numbered 1, the second target j+1 is numbered 2, the nth target j-1 is numbered n, and so on.

[0113] Second, according to the interference constraints and the order of each target to be interfered in the target sequence, the drones in the drone swarm are assigned targets to be interfered and the interference duration of each target to be interfered is divided to obtain the interference planning result corresponding to the target sequence.

[0114] In this embodiment, the targets to be interfered with are sequentially assigned to each UAV according to their order in the target sequence, and the interference duration for each target is allocated. Both the allocation of targets and the interference duration must satisfy the interference constraints, thereby obtaining the interference planning result corresponding to the target sequence. If multiple target sequences are predetermined, the interference planning result for each target sequence is determined in the same way.

[0115] Specifically, in this embodiment, Sep(i) represents the i-th target to be interfered with in the target sequence Sep: [j, j+1, ..., n, 1, ..., j-1], and sets the variable k, which is initialized to 1, i.e., k = 1.

[0116] The process involves iterating through and assigning each target to be interfered with in the target sequence: First, it determines whether adding the target Sep(i) to the interference path of the k-th UAV satisfies the UAV range constraint in the interference conditions. Specifically, this includes the following two cases:

[0117] (1) If adding the target Sep(i) to the interference path of the kth UAV can satisfy the UAV range constraint in the interference constraint, then there are three possible scenarios:

[0118] a) If the interference path of the kth UAV is empty, then directly add the target to be interfered, Sep(i), to the interference path of the kth UAV.

[0119] (b) If a target to be interfered with has already been assigned in the interference path of the k-th UAV, then the target Sep(i) to be interfered with is compared with the target to be interfered with already assigned in the interference path using its left time window (i.e., the earliest interference time), and the target Sep(i) to be interfered with is added to the interference path of the k-th UAV in ascending order of its left time window. For example, if the left time window of the target Sep(i) to be interfered with is less than the left time window of the target to be interfered with already assigned in the interference path, then the target Sep(i) to be interfered with is added before the target to be interfered with already assigned in the interference path of the k-th UAV; if the left time window of the target Sep(i) to be interfered with is not less than the left time window of the target to be interfered with already assigned in the interference path, then the target Sep(i) to be interfered with is added after the target to be interfered with already assigned in the interference path of the k-th UAV.

[0120] c) If the number l of the assigned targets to be interfered with in the interference path of the k-th UAV is greater than 1, then the left time windows of the two assigned targets to be interfered with at the beginning and end of the interference path of the k-th UAV need to be compared with the left time window of the target to be interfered with Sep(i). If the left time window of the target to be interfered with Sep(i) is not greater than the left time window of the target to be interfered with at the beginning of the interference path of the k-th UAV, then the target to be interfered with Sep(i) is added to the beginning of the interference path of the k-th UAV; if the left time window of the target to be interfered with Sep(i) is not less than the left time window of the target to be interfered with at the end of the interference path of the k-th UAV, then the target to be interfered with Sep(i) is added to the end of the interference path of the k-th UAV; if the left time window of the target to be interfered with Sep(i) is greater than 1, then the left time window of the target to be interfered with Sep(i) is added to the end of the interference path of the k-th UAV. Between the left time window of the target to be interfered at the first position and the left time window of the target to be interfered at the last position in the interference path of the kth UAV, it is necessary to traverse the middle insertion position of every two adjacent targets to be interfered in the interference path of the kth UAV to determine whether there is a middle insertion position where the target to be interfered Sep(i) can be inserted. That is, whether there is a situation where the left time window of the target to be interfered Sep(i) is between the left time windows of two adjacent targets to be interfered. If it exists, the target to be interfered Sep(i) is added between these two adjacent targets to be interfered in the interference path of the kth UAV. If it does not exist, a new path (the interference path of the k+1th UAV) needs to be opened, and the target to be interfered Sep(i) is added to the interference path of the k+1th UAV, that is, the target to be interfered Sep(i) is assigned to the k+1th UAV.

[0121] (2) If adding the target Sep(i) to the interference path of the kth UAV does not satisfy the UAV range constraint in the interference constraint (i.e., if adding the target Sep(i) to the interference path of the kth UAV will cause the range of the kth UAV to complete the interference path to exceed the maximum range of the UAV), it means that the allocation of the target to be interfered for the kth UAV has been completed and the interference path of the kth UAV has been obtained. k needs to be updated, i.e., k = k + 1. Continue to execute the above steps and use the remaining targets to be interfered in the target sequence to allocate the target to the next UAV until all targets to be interfered in the target sequence have been allocated.

[0122] After allocating the targets to be interfered with in the target sequence using the above method, the interference path of each UAV can be determined. Then, according to the interference constraints, the interference duration of the targets to be interfered with in each interference path is allocated, thereby obtaining the interference planning result of the target sequence. For example, in allocating the interference duration of the targets to be interfered with according to the interference constraints, it is necessary to ensure that any two adjacent targets to be interfered with in the interference path meet the following conditions: the first target to be interfered with and the second target to be interfered with are two adjacent targets to be interfered with in the same interference path, and the first target to be interfered with is before the second target to be interfered with. Then, the range corresponding to the left time window of the first target to be interfered with plus the interference duration of the first target to be interfered with and the right time window of the first target to be interfered with plus the interference duration of the first target to be interfered with should be within the range corresponding to the left time window and the right time window of the second target to be interfered with.

[0123] Third, at least one interference planning result is used as the interference planning population of the UAV swarm.

[0124] The above steps can determine the interference planning results corresponding to each target sequence. Then, the set of each interference planning result is used to obtain the interference planning population of the UAV swarm. That is, the interference planning population of the UAV swarm includes at least one interference planning result, and the interference planning result includes the interference path of the UAV and the interference duration of the UAV (that is, the interference duration of each target to be interfered with in the interference path).

[0125] As an optional implementation, this embodiment can use a pre-set chromosome encoding rule to encode the interference planning results. Since the interference planning results include interference paths and interference durations, the chromosome representing the interference planning results is divided into two segments: one segment is the path chromosome segment, and the other segment is the duration chromosome segment.

[0126] The path chromosome segment of the interference planning result contains the interference path of at least one UAV. The interference path of each UAV is represented by the target numbers to be interfered with, arranged in the order of interference of each target of the UAV. For example, the path chromosome segment is: 0, 3, 2, 6, 0, 7, 1, 5, 0, 8, 4, 0. In the path chromosome segment, the number 0 represents the starting point, and each other number represents a target to be interfered with. Each target to be interfered with is pre-labeled with a number, and the path chromosome segment can directly use the number to represent the interference path. The path chromosome segment indicates that the interference planning results include interference paths for three drones. The interference path of the first drone is 0-3-2-6-0, which means that the first drone starts from the starting point 0 and performs interference tasks on the three targets in the order of target 3, target 2, and target 6. After completing the interference tasks on all three targets, it returns from target 6 to the starting point 0. The interference path of the second drone is 0-7-1-5-0, which means that the second drone starts from the starting point 0 and performs interference tasks on the three targets in the order of target 7, target 1, and target 5. After completing the interference tasks on all three targets, it returns from target 5 to the starting point 0. The interference path of the third drone is 0-8-4-0, which means that the third drone starts from the starting point 0 and performs interference tasks on the two targets in the order of target 8 and target 4. After completing the interference tasks on the two targets, it returns from target 4 to the starting point 0.

[0127] The duration chromosome segment contains interference duration numbers for each target to be interfered with, arranged in the interference order along the UAV's interference path. The interference duration numbers can be set by discretizing the interference time, dividing the maximum interference time into equal parts. For example, if the maximum interference time is 1 hour, dividing it into 8 equal parts, each part represents 7.5 minutes. When the interference duration number is 1, the interference duration is 7.5 minutes; when the interference duration number is 2, the interference duration is 15 minutes, and so on. If the path chromosome segment is: 0, 3, 2, 6, 0, 7, 1, 5, 0, 8, 4, 0, the corresponding duration chromosome segment is: 3, 7, 2, 5, 3, 6, 2, 4. The first drone's interference durations for targets 3, 2, and 6 were labeled 3, 7, and 2, respectively, with corresponding interference durations of 22.5 minutes, 52.5 minutes, and 15 minutes. The second drone's interference durations for targets 7, 1, and 5 were labeled 5, 3, and 6, respectively, with corresponding interference durations of 37.5 minutes, 22.5 minutes, and 45 minutes. The third drone's interference durations for targets 8 and 4 were labeled 2 and 4, respectively, with corresponding interference durations of 15 minutes and 30 minutes.

[0128] S103. Based on the interference planning population, as well as the pre-set population expansion rules and population selection rules, determine the target interference planning result.

[0129] In this embodiment, the interference planning results in the pre-determined interference planning population of the UAV swarm are all randomly generated. Therefore, this interference planning population is an initialized population, and the number of interference planning results in the population is limited, unable to encompass more planning possibilities. Therefore, this embodiment needs to utilize pre-set population expansion rules to expand the better interference planning results in the interference planning population, thereby obtaining more interference planning results. Then, using pre-set population selection rules, the best interference planning results are selected from all the initial and expanded interference planning results as the final target interference planning result. The population expansion rules can use selection, crossover, and mutation methods from genetic algorithms to update the interference planning population, expanding it to obtain a new interference planning population. The population selection rules include rules for selecting interference planning results based on the interference function values ​​of each interference planning result in the interference planning population, for example, selecting interference planning results with excellent interference function values. The interference function values ​​of the interference planning results are calculated based on a pre-constructed interference planning objective function.

[0130] Furthermore, this step specifically includes the following steps:

[0131] First, based on the interference planning population and the pre-set population expansion and selection rules, the interference planning population is iteratively updated, and the interference planning population that reaches the preset iteration termination condition is taken as the target interference planning population.

[0132] This embodiment can expand the interference planning population using pre-set population expansion rules. For example, a genetic algorithm can be used to select, crossover, and mutate the interference planning results in the interference planning population to adjust the planning results and obtain an adjusted population. The selection, crossover, and mutation methods in the genetic algorithm are existing techniques and will not be specifically described in this embodiment. Then, using the population selection rules, interference planning results with better interference function values ​​are extracted from all interference planning results contained in the interference planning population and the planned population to form a new interference planning population, thereby updating the interference planning population. The population is iteratively updated through the above-mentioned method of updating the interference planning population, and a preset iteration termination condition is set. After each update of the interference planning population, the current iteration condition is updated simultaneously until the current iteration condition reaches the preset iteration termination condition, at which point the iterative update of the interference planning population stops, and the last updated interference planning population is taken as the target interference planning population. The preset iteration termination condition can be a preset number of iterations. The current iteration condition is the current number of iterations. Each iteration update increments the current number of iterations by 1. When the current number of iterations reaches the preset number of iterations, it means that the current iteration condition has reached the preset iteration termination condition.

[0133] Second, using population selection rules, the target interference planning results are selected from the target interference planning population.

[0134] After obtaining the target interference planning population through iterative updates of the interference planning population described above, it is necessary to use population selection rules to select the target interference planning result with the optimal interference function value from all interference planning results in the target interference planning population. The smaller the interference function value, the better. This step specifically includes:

[0135] First, based on the interference function values ​​of each interference planning result in the target interference planning population, the non-dominated order of each interference planning result in the target interference planning population is determined by performing a non-dominated sort.

[0136] This embodiment uses a pre-constructed interference planning objective function to calculate the interference function value of each interference planning result. Based on the interference function value of each interference planning result, the dominance relationship between each interference planning result in the target interference planning population is determined, thereby determining the number of dominated interference planning results for each interference planning result. The number of dominated interference planning results represents the number of other interference planning results in the target interference planning population that dominate that interference planning result. Furthermore, if one interference planning result dominates another interference planning result, it means that the interference function value of one interference planning result is better than the interference function value of the other interference planning result.

[0137] Since the interference planning objective function is constructed based on the interference path and interference benefits of the UAV swarm, the interference planning objective function includes the interference path planning function and the interference duration planning function. Therefore, the interference function value includes the interference path function value calculated using the interference path planning function and the interference duration function value calculated using the interference duration planning function.

[0138]

[0139]

[0140] f2(x)=1 / ∑ k∈K ∑ i∈V t ik p i

[0141] In the above formula, f(x) is the interference planning objective function, f1(x) is the interference path planning function, and f2(x) is the interference duration planning function. For the first and second interference planning results, this embodiment can set at least one of the interference path function value and interference duration function value of the first interference planning result to be better than the second interference planning result, indicating that the interference function value of the first interference planning result is better than the interference function value of the second interference planning result. Specifically, if the first interference path function value is less than the second interference path function value, it means that the first interference path function value is better than the second interference path function value; if the first interference duration function value is less than the second interference duration function value, it means that the first interference duration function value is better than the second interference duration function value. This embodiment can also set both the interference path function value and the interference duration function value of the first interference planning result to be better than the second interference planning result, indicating that the interference function value of the first interference planning result is better than the interference function value of the second interference planning result. If the interference path function value of the first interference planning result is better than the interference path function value of the second interference planning result, but the interference duration function value of the first interference planning result is not better than the interference path function value of the second interference planning result, then it is necessary to determine the first difference between the interference path function values ​​of the first and second interference planning results and the second difference between the interference duration function value of the first and second interference planning results. If the first difference is greater than the second difference, then it is determined that the interference function value of the first interference planning result is better than the interference function value of the second interference planning result. If the first difference is less than the second difference, then it is determined that the interference function value of the second interference planning result is better than the interference function value of the first interference planning result. If the two differences are equal, then the two interference planning results are not assigned to each other.

[0142] By analyzing the number of dominated participants in each interference planning outcome within the target interference planning population, the non-dominated planning outcomes are ranked, and their non-dominated order is determined based on this ranking. For example, a smaller number of dominated participants indicates a better interference planning outcome, and a higher non-dominated order. A non-dominated order of 1 represents the highest level, 2 the next highest, and so on; the larger the non-dominated order value, the lower the level.

[0143] Then, the interference planning result with the highest non-dominant order in the target interference planning population is taken as the target interference planning result.

[0144] After determining the non-dominated order of each interference planning result in the target interference planning population through the above steps, the interference planning result with the highest level of non-dominated order in the target interference planning population is taken as the target interference planning result, that is, the interference planning result with a non-dominated order of 1 in the target interference planning population is taken as the target interference planning result.

[0145] If there are multiple interference planning results with the highest non-dominated order in the final target interference planning population, i.e., multiple target interference planning results, a Pareto optimal front diagram can be constructed to observe the differences between these results and select the desired one. The Pareto optimal front diagram is constructed by approximately normalizing both the interference path function and interference duration function values ​​of the target interference planning results, bringing the function values ​​to the same order of magnitude. The Pareto optimal front diagram is then constructed using the interference path planning function and interference duration planning function as the X and Y axes, respectively. The X-axis represents the normalized value of the interference path function corresponding to the target interference planning result on the interference path planning function, and the Y-axis represents the normalized value of the interference duration function corresponding to the target interference planning result on the interference duration planning function.

[0146] This embodiment can also determine the relative importance of interference path and interference duration based on interference planning requirements, and assign weights to the interference path planning function and interference duration planning function according to their importance. For example, if interference path planning is more important than interference duration planning, the weight of the interference path planning function is set to be greater than the weight of the interference duration planning function; if interference duration planning is more important than interference path planning, the weight of the interference duration planning function is set to be greater than the weight of the interference path planning function; if interference path planning and interference duration planning are equally important, the weight of the interference path planning function is set to be equal to the weight of the interference duration planning function, i.e., both weights are 0.5. Then, based on the weights of the two functions, the two function values ​​of the target interference planning result are weighted and summed to obtain the final function value of each target interference planning result, and the target interference planning result with the smallest final function value is taken as the final target interference planning result used for implementation.

[0147] As described above, the UAV swarm interference planning method proposed in this application constructs an interference planning objective function for the UAV swarm based on the path length and interference benefits of the UAV swarm, and constructs interference constraints for the UAV swarm based on preset UAV interference rules; based on the interference constraints and the target to be interfered with, it determines the interference planning population of the UAV swarm; the interference planning population includes interference planning results of at least one UAV swarm, and the interference planning results include the interference path and interference duration of at least one UAV; based on the interference planning population, and preset population expansion rules and population selection rules, it determines the target interference planning result; the population selection rules include rules for selection based on the interference function values ​​of each interference planning result calculated using the interference planning objective function. By adopting the technical solution of this application, the path length and interference benefits of the UAV swarm can be used as planning objectives, while simultaneously planning the interference path and interference duration of the UAV swarm, thereby optimizing the UAV path and interference duration, reducing the interference cost of UAVs, increasing the interference benefits of UAVs, and ultimately improving the interference effect of the UAV swarm performing interference tasks.

[0148] As an optional implementation, see [link to implementation details]. Figure 2 As shown, in another embodiment of this application, step S103 involves iteratively updating the interference planning population based on the interference planning population and pre-set population expansion and selection rules, and using the interference planning population that reaches the preset iteration termination condition as the target interference planning population. This specifically includes the following steps:

[0149] S201. Based on the pre-set population expansion rules, adjust the interference planning results in the interference planning population to obtain the subpopulation corresponding to the interference planning population, and merge the interference planning population and the subpopulation corresponding to the interference planning population to obtain the merged population.

[0150] This embodiment utilizes pre-set population expansion rules to adjust the interference planning results in the interference planning population, thereby obtaining the adjusted interference planning results. The population formed by these adjusted interference planning results is then used as the subpopulation corresponding to the interference planning population. The population expansion rules are selection, crossover, and mutation adjustments used in genetic algorithms, which are existing techniques and will not be elaborated upon in this embodiment. Furthermore, the number of interference planning results in the subpopulation corresponding to the interference planning population is the same as the number of interference planning results in the interference planning population. This embodiment merges the interference planning population and its corresponding subpopulation to obtain a merged population, which includes both the interference planning results before and after the adjustment.

[0151] like Figure 3 As shown, Pt represents the interference planning population, Qt represents the subpopulation corresponding to the interference planning population, and Rt, formed by merging Pt and Qt, represents the merged population. Here, t represents the iteration number. The initial interference planning population has 0 iterations, denoted as P0, and the subpopulation corresponding to the initial interference planning population is denoted as Q0. Therefore, the merged population obtained by merging the initial interference planning population P0 and the corresponding subpopulation Q0 is denoted as R0.

[0152] Furthermore, for the subpopulations obtained using the population expansion rule, it is necessary to determine whether each interference planning result in the subpopulation meets the interference constraints. If there are interference paths in the interference planning results that do not meet the interference constraints, the interference path needs to be adjusted according to the interference constraints. If there are conflicting targets in the interference path that cannot perform interference tasks, the targets can be assigned to other UAVs or to a new UAV, or a new interference path can be created, etc., to ensure that each interference planning result in the subpopulation meets the interference constraints.

[0153] S202. Based on the pre-set population selection rules, select a preset number of interference planning results from the merged population as the iteratively updated interference planning population.

[0154] This embodiment utilizes a pre-set population selection rule to select a predetermined number of interference planning results from the merged population, and uses the population composed of these selected interference planning results as the iteratively updated interference planning population. The predetermined number is preferably set to the number of interference planning results included in the initial interference planning population. Selecting interference planning results from the merged population requires choosing a predetermined number of interference planning results with the best interference function values ​​based on the interference function values ​​of each interference planning result in the merged population. The specific steps are as follows:

[0155] First, using the interference planning objective function, calculate the interference function value of each interference planning result in the merged population.

[0156] This embodiment utilizes an interference planning objective function to calculate the interference function value of each interference planning result in the merged population. The interference planning objective function includes an interference path planning function and an interference duration planning function; the interference function value includes the interference path function value and the interference duration function value.

[0157] Second, based on the interference function values ​​of each interference planning result, the non-dominated order of each interference planning result is determined, and the congestion degree of each interference planning result is calculated.

[0158] Based on the calculated interference function values ​​of each interference planning result, all interference planning results in the merged population are sorted using a non-dominated order to determine the non-dominated order of each interference planning result. The method for determining the non-dominated order of the interference planning results in the merged population is the same as the method for determining the non-dominated order of the interference planning results in the target interference planning population described in the previous embodiment, and will not be elaborated upon in this embodiment.

[0159] This embodiment also calculates the crowding degree of each interference planning result in the merged population based on the interference function value of each interference planning result, where the crowding degree represents the density of the interference planning result in the merged population. For interference planning results with the same non-dominant order, the greater the crowding degree of the interference planning result, the greater the crowding distance, the higher the fitness, and the better the interference planning result. The calculation of the crowding degree of the interference planning result specifically includes:

[0160] First, calculate the first difference between the interference path function values ​​and the second difference between the interference duration function values ​​of two interference planning results adjacent to the interference planning result.

[0161] Retrieve the two interference planning results adjacent to the current interference planning result, i.e., the previous interference planning result and the next interference planning result. Calculate the interference path function values ​​for the previous and next interference planning results according to the interference path planning function, and use the difference between them as the first difference. Similarly, calculate the interference duration function values ​​for the previous and next interference planning results according to the interference duration planning function, and use the difference between them as the second difference.

[0162] Secondly, based on the interference path function value and interference duration function value of all interference planning results in the merged population, the third difference between the maximum interference path function value and the minimum interference path function value, and the fourth difference between the maximum interference duration function value and the minimum interference duration function value, are determined.

[0163] According to the interference path planning function, the interference path function values ​​of all interference planning results in the merged population are calculated. The maximum value is selected as the maximum interference path function value, and the minimum value is selected as the minimum interference path function value. Finally, the third difference between the maximum and minimum interference path function values ​​is calculated.

[0164] According to the interference duration planning function, the interference duration function values ​​of all interference planning results in the merged population are calculated. The maximum value is selected as the maximum interference duration function value, and the minimum value is selected as the minimum interference duration function value. The fourth difference between the maximum and minimum interference duration function values ​​is then calculated.

[0165] Finally, the ratio between the first and third differences is taken as the first congestion degree of the interference planning result on the interference path planning function, the ratio between the second and fourth differences is taken as the second congestion degree of the interference planning result on the interference duration planning function, and the sum of the first and second congestion degrees is taken as the congestion degree of the interference planning result.

[0166] The formulas for calculating both the first and second levels of congestion are:

[0167]

[0168] Where, when m represents the interference path planning function, I mLet I[i+1]·m represent the first congestion level, I[i-1]·m represent the interference path function value of the next interference planning result, I[i-1]·m represent the interference path function value of the previous interference planning result, and I[i+1]·mI[i-1]·m represent the first difference. This represents the value of the maximum interference path function. This represents the minimum interference path function value. This represents the third difference. When m represents the interference duration planning function, I m Let I[i+1]·m represent the second congestion level, I[i-1]·m represent the interference duration function value of the next interference planning result, I[i-1]·m represent the interference duration function value of the previous interference planning result, and I[i+1]·mI[i-1]·m represent the second difference. This represents the function value of the maximum interference duration. This represents the minimum interference duration function value. This represents the fourth difference.

[0169] Therefore, the formula for calculating the congestion level of the interference planning results is:

[0170]

[0171] Among them, I distance This indicates the degree of congestion that interferes with the planning results.

[0172] Third, based on the non-dominated order and crowding degree of each interference planning result, a preset number of interference planning results are selected from the merged population as the iteratively updated interference planning population.

[0173] Based on the non-dominated order and crowding degree of each disturbance planning result in the merged population, a predetermined number of disturbance planning results are selected from the merged population. First, selection is made from the disturbance planning results with the highest non-dominated order. If the number of disturbance planning results with the highest non-dominated order is greater than the predetermined number, these results are sorted by crowding degree from largest to smallest, and the predetermined number of disturbance planning results at the top of the sorted list are selected. If the number of disturbance planning results with the highest non-dominated order is not greater than the predetermined number, all disturbance planning results with the highest non-dominated order are selected, and the predetermined number is subtracted from the selected number to obtain the remaining number. Then, the remaining number of disturbance planning results is selected from all disturbance planning results of the next lower level of non-dominated order. If the number of disturbance planning results of the next lower level of non-dominated order is greater than the remaining number, these results are sorted by crowding degree from largest to smallest, and the remaining number of disturbance planning results at the top of the sorted list are selected. If the number of interference planning results of the next level of non-dominated order is not greater than the remaining number, then all interference planning results of the next level of non-dominated order are selected, and the number of all selected interference planning results is subtracted from the preset number to obtain the updated remaining number. Then, the remaining number of interference planning results is selected from all interference planning results of the next level of non-dominated order, and so on, until the number of selected interference planning results reaches the preset number.

[0174] like Figure 3 As shown, F1 represents all interference planning results at the highest non-dominated order level (i.e., non-dominated order is 1), F2 represents all interference planning results at the next lower level (i.e., non-dominated order is 2), and F3 represents all interference planning results at the next lower level (i.e., non-dominated order is 3). If the number of interference planning results in F1 is less than a preset number, all interference planning results in F1 are selected. If the number of interference planning results in F2 is less than the remaining number, all interference planning results in F1 are selected. If the number of interference planning results in F3 is greater than the remaining number, all interference planning results in F3 are sorted from highest to lowest crowding. The remaining interference planning results at the top of the sorted list are selected, resulting in the preset number of interference planning results. The population formed by all selected interference planning results is used as the iteratively updated interference planning population Pt+1. The iteration count is incremented by 1 to update the iteration count.

[0175] S203. Determine whether the current iteration conditions have met the preset iteration end conditions; if yes, proceed to step S204; if no, continue to proceed to step S201.

[0176] This embodiment needs to determine the current iteration conditions after updating the aforementioned interference planning population, such as the current number of iterations, and then determine whether the current iteration conditions have reached the preset iteration end condition, such as whether the current number of iterations has reached the preset iteration end number. If the current iteration conditions have reached the preset iteration end condition, then step S204 is executed. If the current iteration conditions have not reached the preset iteration end condition, then step S201 is executed again, that is, based on the population expansion rule, the interference planning results in the iteratively updated interference planning population are adjusted to determine the updated merged population, and based on the pre-set population selection rule, a preset number of interference planning results are selected from the updated merged population as the iteratively updated interference planning population.

[0177] S204. Use the iteratively updated interference planning population as the target interference planning population.

[0178] If the current iteration conditions meet the preset iteration termination conditions, then the interference planning population obtained from the last update will be used as the target interference planning population.

[0179] As an optional implementation, see [link to implementation details]. Figure 4 As shown in another embodiment of this application, the specific steps for determining the non-dominated order of each interference planning result by performing a non-dominated sort based on the interference function values ​​of each interference planning result in step S202 are as follows:

[0180] S401. Determine the number of dominated individuals in each interference planning result based on the interference function values ​​of the interference planning results in the merged population.

[0181] In this embodiment, the number of dominated interference planning results in the merged population is determined based on the interference function values ​​of each interference planning result calculated using the interference planning objective function. The number of dominated interference planning results represents the number of other planning results in the merged population that dominate that interference planning result. Furthermore, for two interference planning results, a first interference planning result and a second interference planning result, if the interference function value of the first interference planning result is better than that of the second interference planning result, then the first interference planning result dominates the second interference planning result; conversely, if the interference function value of the second interference planning result is better than that of the first interference planning result, then the second interference planning result dominates the first interference planning result. By determining this dominance relationship, the dominant set corresponding to each interference planning result can be determined. This dominant set includes other interference planning results in the merged population that dominate that interference planning result, and the number of interference planning results included in the dominant set corresponding to each interference planning result is the number of dominated interference planning results.

[0182] S402. Based on all currently determined non-dominated orders, determine the non-dominated order of the disturbance planning results with a dominated number of 0.

[0183] After determining the number of dominated individuals for each interference program outcome in the merged population, it is necessary to determine the non-dominated order for the interference program outcomes with a dominated number of 0 based on all currently determined non-dominated orders. For example, if there are no interference program outcomes with a determined non-dominated order, then the non-dominated order of all interference program outcomes with a dominated number of 0 in the merged population at this time is determined to be 1; if an interference program outcome with a non-dominated order of 1 has been determined, then the non-dominated order of all interference program outcomes with a dominated number of 0 in the merged population at this time is determined to be 2; if interference program outcomes with non-dominated orders of 1 and 2 have been determined, then the non-dominated order of all interference program outcomes with a dominated number of 0 in the merged population at this time is determined to be 3; and so on.

[0184] S403. Remove the interference programming results with determined non-dominant order from the merged population.

[0185] Since the non-dominated order of the interference program results has already been determined, there is no need to determine the non-dominated order again. Therefore, the interference program results with the determined non-dominated order need to be removed from the merged population to obtain the updated merged population.

[0186] S404. Determine whether all interference planning results in the merged population have been removed; if yes, proceed to step S405; if no, continue to step S401.

[0187] Determine whether all the interference planning results in the updated merged population have been removed, that is, determine whether there are still interference planning results in the updated merged population. If there are still interference planning results in the updated merged population, then step S401 needs to be executed again to continue the non-dominated sorting of the interference planning results in the updated merged population. That is, continue to determine the number of dominated results of the remaining interference planning results based on the interference function value of the remaining interference planning results in the updated merged population, and determine the non-dominated order of the interference planning results with a number of dominated 0 among the remaining interference planning results based on all the currently determined non-dominated orders.

[0188] S405, End.

[0189] If there are no interfering program results in the updated merged population, that is, all interfering program results in the merged population have been removed, it means that all interfering program results have been determined to have a non-dominated order, and the non-dominated sorting process ends at this time.

[0190] In the above embodiments, the non-dominated ordering of each interference planning result in the target interference planning population is performed, and the specific execution method of the non-dominated ordering of each interference planning result is determined. Figure 4 The execution method of the illustrated embodiments is the same.

[0191] Exemplary device

[0192] Accordingly, embodiments of this application also provide a planning device for unmanned aerial vehicle (UAV) swarm interference, see [link to relevant documentation]. Figure 5 As shown, the device includes:

[0193] Module 100 is used to construct the interference planning objective function of the UAV swarm based on the path length and interference benefit of the UAV swarm, and to construct the interference constraints of the UAV swarm based on the preset UAV interference rules.

[0194] The population determination module 110 is used to determine the interference planning population of the UAV swarm based on interference constraints and the target to be interfered with; the interference planning population includes the interference planning results of at least one UAV swarm, and the interference planning results include the interference path and interference duration of at least one UAV.

[0195] The planning result determination module 120 is used to determine the target interference planning result based on the interference planning population and the pre-set population expansion rules and population selection rules. The population selection rules include rules for selecting interference planning results based on the interference function values ​​of each interference planning result. The interference function values ​​of the interference planning results are calculated based on the interference planning objective function.

[0196] As can be seen from the above description, the drone swarm interference planning device proposed in this application can plan the path length and interference benefits of the drone swarm as the planning objectives, and simultaneously plan the interference path and interference duration of the drone swarm, thereby optimizing the drone path and interference duration, reducing the interference cost of the drone swarm, and improving the interference benefits of the drone swarm performing interference tasks.

[0197] As an optional implementation, another embodiment of this application discloses that the planning result determination module 120 includes: a population iterative update unit and a planning result selection unit.

[0198] The population iterative update unit is used to iteratively update the interference planning population based on the interference planning population and the pre-set population expansion rules and population selection rules, and to take the interference planning population that reaches the preset iteration end condition as the target interference planning population.

[0199] The planning result selection unit is used to select the target interference planning result from the target interference planning population using population selection rules.

[0200] As an optional implementation, another embodiment of this application discloses a population iterative update unit, specifically used for:

[0201] Based on the pre-set population expansion rules, the interference planning results in the interference planning population are adjusted to obtain the subpopulation corresponding to the interference planning population. The interference planning population and the subpopulation corresponding to the interference planning population are then merged to obtain the merged population.

[0202] Based on the pre-set population selection rules, a preset number of interference planning results are selected from the merged population as the interference planning population after iterative update, and it is determined whether the current iteration conditions have reached the preset iteration end conditions.

[0203] If the current iteration conditions meet the preset iteration termination conditions, then the iteratively updated interference planning population will be used as the target interference planning population.

[0204] If the current iteration conditions do not meet the preset iteration end conditions, then continue to adjust the interference planning results in the iteratively updated interference planning population based on the population expansion rules, determine the updated merged population, and select a preset number of interference planning results from the updated merged population as the iteratively updated interference planning population based on the preset population selection rules.

[0205] As an optional implementation, another embodiment of this application discloses that the population iterative update unit includes: a function calculation unit, a determination unit, and a selection unit.

[0206] The function computation unit is used to calculate the interference function value of each interference planning result in the merged population using the interference planning objective function;

[0207] The determination unit is used to perform non-dominated sorting of each interference planning result based on the interference function value of each interference planning result, determine the non-dominated order of each interference planning result, and calculate the crowding degree of each interference planning result; the crowding degree of the interference planning result represents the density of the interference planning result in the merged population.

[0208] The selection unit is used to select a preset number of interference planning results from the merged population as the iteratively updated interference planning population according to the non-dominated order and crowding degree of each interference planning result.

[0209] As an optional implementation, another embodiment of this application discloses a determining unit, specifically used for:

[0210] Based on the interference function values ​​of each interference planning result in the merged population, the number of dominated interference planning results is determined; the number of dominated interference planning results represents the number of other interference planning results that dominate interference planning results in the merged population; the first interference planning result dominating the second interference planning result means that the interference function value of the first interference planning result is better than the interference function value of the second interference planning result.

[0211] Based on all currently determined non-dominated orders, determine the non-dominated order of the disturbance planning results with a dominated number of 0;

[0212] Remove the interference planning results with the determined non-dominated order from the merged population. Continue to determine the number of the remaining interference planning results based on the interference function value of the remaining interference planning results in the merged population. Also, determine the non-dominated order of the interference planning results with a number of 0 among the remaining interference planning results based on all the currently determined non-dominated orders, until all interference planning results in the merged population are removed.

[0213] As an optional implementation, another embodiment of this application discloses that the interference planning objective function includes: an interference path planning function and an interference duration planning function, and the interference function value includes: an interference path function value and an interference duration function value;

[0214] Determining the unit, specifically also used for:

[0215] Calculate the first difference between the interference path function values ​​and the second difference between the interference duration function values ​​of two interference planning results adjacent to the interference planning result;

[0216] Based on the interference path function value and interference duration function value of all interference planning results in the merged population, determine the third difference between the maximum interference path function value and the minimum interference path function value, and the fourth difference between the maximum interference duration function value and the minimum interference duration function value.

[0217] The ratio between the first and third differences is taken as the first congestion degree of the interference planning result on the interference path planning function, the ratio between the second and fourth differences is taken as the second congestion degree of the interference planning result on the interference duration planning function, and the sum of the first and second congestion degrees is taken as the congestion degree of the interference planning result.

[0218] As an optional implementation, another embodiment of this application discloses a planning result selection unit, specifically used for:

[0219] Based on the interference function values ​​of each interference planning result in the target interference planning population, the non-dominated order of each interference planning result in the target interference planning population is determined by performing a non-dominated sort.

[0220] The interference planning result with the highest non-dominant order in the target interference planning population is taken as the target interference planning result.

[0221] As an optional implementation, another embodiment of this application discloses a population determination module 110, specifically used for:

[0222] All targets to be interfered with in a pre-set interference task are randomly arranged to form at least one target sequence;

[0223] According to the interference constraints and the order of each target to be interfered in the target sequence, the UAVs in the UAV swarm are assigned targets to be interfered and the interference duration of each target to be interfered is divided, so as to obtain the interference planning results corresponding to the target sequence.

[0224] At least one interference planning result is used as the interference planning population of the UAV swarm.

[0225] As an optional implementation, another embodiment of this application discloses that the interference planning results are represented using a pre-set chromosome encoding rule;

[0226] The interference planning results include path chromosome segments and duration chromosome segments;

[0227] The path chromosome segment contains at least one UAV interference path, and the UAV interference path includes: the sequence number of the targets to be interfered with arranged in the order of interference of each target of the UAV.

[0228] The duration chromosome segment contains interference duration labels for the targets to be interfered with, arranged in the order of the targets to be interfered with in the path chromosome segment.

[0229] The UAV swarm interference planning device provided in this embodiment belongs to the same application concept as the UAV swarm interference planning method provided in the above embodiments of this application. It can execute the UAV swarm interference planning method provided in any of the above embodiments of this application and has the corresponding functional modules and beneficial effects for executing the UAV swarm interference planning method. Technical details not described in detail in this embodiment can be found in the specific processing content of the UAV swarm interference planning method provided in the above embodiments of this application, and will not be repeated here.

[0230] Exemplary electronic devices

[0231] Another embodiment of this application also provides an electronic device, see [link to relevant documentation] Figure 6 As shown, the device includes:

[0232] Memory 200 and processor 210;

[0233] The memory 200 is connected to the processor 210 and is used to store programs;

[0234] The processor 210 is configured to implement the planning method for unmanned aerial vehicle (UAV) swarm interference disclosed in any of the above embodiments by running the program stored in the memory 200.

[0235] Specifically, the aforementioned electronic device may also include: a bus, a communication interface 220, an input device 230, and an output device 240.

[0236] The processor 210, memory 200, communication interface 220, input device 230, and output device 240 are interconnected via a bus. Among them:

[0237] A bus can include a pathway for transmitting information between various components of a computer system.

[0238] The processor 210 can be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present invention. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0239] Processor 210 may include a main processor, as well as a baseband chip, modem, etc.

[0240] The memory 200 stores a program that executes the technical solution of this invention, and may also store an operating system and other key business functions. Specifically, the program may include program code, which includes computer operation instructions. More specifically, the memory 200 may include read-only memory (ROM), other types of static storage devices capable of storing static information and instructions, random access memory (RAM), other types of dynamic storage devices capable of storing information and instructions, disk storage, flash memory, etc.

[0241] Input device 230 may include a device for receiving user input data and information, such as a keyboard, mouse, camera, scanner, light pen, voice input device, touch screen, pedometer, or gravity sensor.

[0242] Output device 240 may include devices that allow information to be output to a user, such as a display screen, printer, speaker, etc.

[0243] The communication interface 220 may include a device that uses any transceiver to communicate with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.

[0244] The processor 210 executes the program stored in the memory 200 and calls other devices, which can be used to implement the various steps of any of the UAV swarm interference planning methods provided in the above embodiments of this application.

[0245] Exemplary computer program products and storage media

[0246] In addition to the methods and devices described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the planning methods for drone swarm interference according to various embodiments of this application as described in the "Exemplary Methods" section of this specification.

[0247] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0248] Furthermore, embodiments of this application may also be storage media storing a computer program, which is executed by a processor in the steps of the planning method for drone swarm interference according to various embodiments of this application described in the "Exemplary Methods" section above.

[0249] For the foregoing method embodiments, in order to simplify the description, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0250] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For apparatus embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0251] The steps in the methods of the various embodiments of this application can be adjusted, merged, or deleted in order according to actual needs, and the technical features described in each embodiment can be replaced or combined.

[0252] The modules and sub-modules in the various embodiments of the present application's devices and terminals can be merged, divided, and deleted according to actual needs.

[0253] It should be understood that the disclosed terminals, devices, and methods can be implemented in other ways, given the several embodiments provided in this application. For example, the terminal embodiments described above are merely illustrative. For instance, the division of modules or sub-modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple sub-modules or modules may be combined or integrated into another module, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0254] The modules or submodules described as separate components may or may not be physically separate. The components that constitute a module or submodule may or may not be physical modules or submodules; that is, they may be located in one place or distributed across multiple network modules or submodules. Some or all of the modules or submodules can be selected to achieve the purpose of this embodiment's solution, depending on actual needs.

[0255] Furthermore, the functional modules or sub-modules in the various embodiments of this application can be integrated into one processing module, or each module or sub-module can exist physically separately, or two or more modules or sub-modules can be integrated into one module. The integrated modules or sub-modules described above can be implemented in hardware or in the form of software functional modules or sub-modules.

[0256] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0257] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software unit executed by a processor, or a combination of both. The software unit can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0258] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0259] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A planning method for interference with unmanned aerial vehicle (UAV) swarms, characterized in that, include: Based on the path length and interference benefit of the UAV swarm, an interference planning objective function for the UAV swarm is constructed, and interference constraints for the UAV swarm are constructed based on preset UAV interference rules. The path length of the UAV swarm and the reciprocal of the interference benefit of the UAV swarm are used as the interference planning objective function. The path length of the UAV swarm is the path length of all UAVs flying to complete the interference tasks of all targets to be interfered with. The interference benefit of the UAV swarm is the interference benefit of all UAVs completing the interference tasks of all targets to be interfered with. The interference benefit of a UAV completing the interference task of a target to be interfered with is the product of the duration of UAV interference with the target and the single-time interference benefit of the target. Based on the interference constraints and the target to be interfered with, an interference planning population of the UAV swarm is determined; the interference planning population includes interference planning results of at least one UAV swarm, and the interference planning results include the interference path and interference duration of at least one UAV. Based on the interference planning population, and the pre-set population expansion rules and population selection rules, the target interference planning result is determined; the population selection rules include rules for selecting interference planning results based on the interference function values ​​of each interference planning result, wherein the interference function values ​​of the interference planning results are calculated based on the interference planning objective function.

2. The method according to claim 1, characterized in that, Based on the interference planning population, and the pre-set population expansion and selection rules, the target interference planning result is determined, including: Based on the interference planning population, as well as the pre-set population expansion rules and population selection rules, the interference planning population is iteratively updated, and the interference planning population that reaches the preset iteration end condition is taken as the target interference planning population. Using the population selection rules, target interference planning results are selected from the target interference planning population.

3. The method according to claim 2, characterized in that, Based on the aforementioned interference planning population, and pre-set population expansion and selection rules, the interference planning population is iteratively updated, and the interference planning population that reaches the preset iteration termination condition is taken as the target interference planning population, including: Based on the pre-set population expansion rules, the interference planning results in the interference planning population are adjusted to obtain the subpopulation corresponding to the interference planning population. The interference planning population and the subpopulation corresponding to the interference planning population are then merged to obtain the merged population. Based on the pre-set population selection rules, a preset number of interference planning results are selected from the merged population as the interference planning population after iterative update, and it is determined whether the current iteration conditions have reached the preset iteration end conditions. If the current iteration conditions meet the preset iteration end conditions, then the iteratively updated interference planning population will be used as the target interference planning population. If the current iteration conditions do not meet the preset iteration end conditions, then based on the population expansion rules, the interference planning results in the iteratively updated interference planning population are adjusted to determine the updated merged population. Based on the preset population selection rules, a preset number of interference planning results are selected from the updated merged population as the iteratively updated interference planning population.

4. The method according to claim 3, characterized in that, Based on pre-set population selection rules, a preset number of interference planning results are selected from the merged population as the iteratively updated interference planning population, including: Using the aforementioned interference planning objective function, the interference function value of each interference planning result in the merged population is calculated; Based on the interference function values ​​of each interference planning result, the non-dominated order of each interference planning result is determined, and the crowding degree of each interference planning result is calculated; the crowding degree of the interference planning result represents the density of the interference planning result in the merged population. Based on the non-dominated order and crowding degree of each interference planning result, a preset number of interference planning results are selected from the merged population as the iteratively updated interference planning population.

5. The method according to claim 4, characterized in that, Based on the interference function values ​​of each interference planning result, the non-dominated order of each interference planning result is determined, including: Based on the interference function values ​​of each interference planning result in the merged population, the number of dominated interference planning results is determined; the number of dominated interference planning results represents the number of other interference planning results that dominate the interference planning result in the merged population; the first interference planning result dominating the second interference planning result means that the interference function value of the first interference planning result is better than the interference function value of the second interference planning result. Based on all currently determined non-dominated orders, determine the non-dominated order of the disturbance planning results with a dominated number of 0; The interference planning results with determined non-dominated orders are removed from the merged population. The number of dominated results of the remaining interference planning results is determined based on the interference function value of the remaining interference planning results in the merged population. The non-dominated order of the interference planning results with a number of dominated 0 in the remaining interference planning results is determined based on all the currently determined non-dominated orders, until all interference planning results in the merged population are removed.

6. The method according to claim 4, characterized in that, The interference planning objective function includes: an interference path planning function and an interference duration planning function, and the interference function value includes: an interference path function value and an interference duration function value; Based on the interference function values ​​of each interference planning result, the congestion degree of the interference planning result is calculated, including: Calculate the first difference between the interference path function values ​​and the second difference between the interference duration function values ​​of two interference planning results adjacent to the interference planning result; Based on the interference path function value and interference duration function value of all interference planning results in the merged population, determine the third difference between the maximum interference path function value and the minimum interference path function value, and the fourth difference between the maximum interference duration function value and the minimum interference duration function value. The ratio between the first difference and the third difference is taken as the first congestion degree of the interference planning result on the interference path planning function, the ratio between the second difference and the fourth difference is taken as the second congestion degree of the interference planning result on the interference duration planning function, and the sum of the first congestion degree and the second congestion degree is taken as the congestion degree of the interference planning result.

7. The method according to claim 2, characterized in that, Using the population selection rule, the target interference planning result is selected from the target interference planning population, including: Based on the interference function values ​​of each interference planning result in the target interference planning population, the non-dominated order of each interference planning result in the target interference planning population is determined by performing a non-dominated sort. The interference planning result with the highest non-dominant order in the target interference planning population is taken as the target interference planning result.

8. The method according to claim 1, characterized in that, Based on the aforementioned interference constraints and the target to be interfered with, the interference planning population of the UAV swarm is determined, including: All targets to be interfered with in a pre-set interference task are randomly arranged to form at least one target sequence; According to the interference constraints and the order of each target to be interfered in the target sequence, the drones in the drone swarm are assigned targets to be interfered and the interference duration of each target to be interfered is divided to obtain the interference planning result corresponding to the target sequence. At least one interference planning result is used as the interference planning population of the UAV swarm.

9. The method according to claim 1, characterized in that, The interference planning results are represented using pre-set chromosome encoding rules; The interference planning results include path chromosome segments and duration chromosome segments; The path chromosome segment contains at least one interference path of a UAV, and the interference path of the UAV includes: the serial numbers of the targets to be interfered with arranged in the interference order of each target to be interfered with by the UAV. The duration chromosome segment contains interference duration labels for the targets to be interfered with, arranged in the order of the targets to be interfered with in the path chromosome segment.

10. A planning device for interfering with unmanned aerial vehicle (UAV) swarms, characterized in that, include: A construction module is used to construct an interference planning objective function for the UAV swarm based on the path length and interference benefit of the UAV swarm, and to construct interference constraints for the UAV swarm based on preset UAV interference rules. Specifically, the path length of the UAV swarm and the reciprocal of the interference benefit of the UAV swarm are used as the interference planning objective function. The path length of the UAV swarm is the path length of all UAVs flying to complete the interference tasks of all targets to be interfered with. The interference benefit of the UAV swarm is the interference benefit of all UAVs completing the interference tasks of all targets to be interfered with. The interference benefit of a UAV completing the interference task of a target to be interfered with is the product of the duration of UAV interference with the target to be interfered with and the single-time interference benefit of the target to be interfered with. The population determination module is used to determine the interference planning population of the UAV swarm based on the interference constraints and the target to be interfered with; the interference planning population includes the interference planning results of at least one UAV swarm, and the interference planning results include the interference path and interference duration of at least one UAV. The planning result determination module is used to determine the target interference planning result based on the interference planning population and the pre-set population expansion rules and population selection rules; the population selection rules include rules for selecting interference planning results based on the interference function values ​​of each interference planning result, and the interference function values ​​of the interference planning results are calculated based on the interference planning objective function.

11. An electronic device, characterized in that, include: Memory and processor; The memory is connected to the processor and is used to store programs; The processor is configured to implement the planning method for drone swarm interference as described in any one of claims 1 to 9 by running a program in the memory.

12. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the planning method for unmanned aerial vehicle (UAV) swarm interference as described in any one of claims 1 to 9.

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