Optimal putting method of smoke screen jamming bomb in multi-machine joint cooperation scene

Abstract

The invention provides an optimal throwing method for smoke screen jamming bombs in a multi-machine joint cooperation scene, and belongs to the field of applied mathematics and computational sciences, and the method comprises the following steps: building a missile trajectory equation according to a known enemy missile motion trajectory; constructing a single-machine single-missile shielding model; solving a maximum effective shielding duration putting strategy under a single-machine single-missile model based on a particle swarm algorithm; constructing a multi-machine joint cooperation shielding model; on the basis of a particle swarm algorithm, a genetic algorithm is fused, the maximum effective shielding duration is solved under the condition that the overlapping shielding duration is reduced as much as possible, and an optimal putting strategy is obtained. According to the method, under the constraint conditions of smoke concentration effectiveness, the shortest time interval of unmanned aerial vehicle launching and the like, the overall shielding duration of the multi-smoke-screen cloud cluster can be remarkably prolonged, and optimal configuration and utilization efficiency maximization of shielding resources are achieved.

Description

Technical Field

[0001] This invention belongs to the field of applied mathematics and computational science, and specifically relates to an optimal method for deploying smoke flares in a multi-machine collaborative scenario. Background Technology

[0002] In modern military defense, the precise delivery strategy of smoke flares plays a crucial role in protecting important targets. Smoke flares rapidly form aerosol clouds in a specific airspace through chemical combustion or explosion, effectively obscuring friendly targets and interfering with the identification and locking of enemy optical observation and precision guidance systems, thereby significantly improving battlefield survivability. With the maturity of unmanned aerial vehicle (UAV) technology, its long endurance, high maneuverability, and precise control have made it a core delivery platform for implementing forward smoke jamming, capable of pinpointing and timing the detonation of flares, creating a dynamic and continuous shielding zone between incoming weapons and the actual targets. The effectiveness of smoke flares lies primarily in the spatiotemporal distribution characteristics of the resulting cloud formation.

[0003] Common deployment strategies, based on the different flight trajectories of drones and the detonation sequence of chaff, can be divided into single-drone single-weapon fixed-point shielding, single-drone multiple-weapon sequential shielding, and multi-drone multiple-weapon coordinated shielding modes, each corresponding to different battlefield scenarios and tactical requirements. The single-drone single-weapon mode is suitable for dealing with a single, clearly defined threat, achieving maximum effective shielding by optimizing the drone's flight path and detonation point; the single-drone multiple-weapon mode forms an extended or overlapping smoke screen through continuous deployment to counter weapon maneuvers or prolong the effective shielding time; the multi-drone multiple-weapon mode is suitable for area protection or dealing with multi-directional saturation attacks, constructing a three-dimensional, continuous shielding system through coordinated deployment.

[0004] In realistic air defense missions, the defending side needs to quickly formulate a deployment plan after radar detects an incoming missile. Upon receiving the mission, the UAV can instantly adjust its course and speed to fly towards the designated deployment point. After detaching from the UAV, the smoke and flares fall along a ballistic trajectory, detonating to form a spherical cloud that descends at a fixed speed, creating an effective shielding zone within a certain area at its center. By analyzing the trajectory and velocity of the incoming missile and the relative positions of the UAV and the target, the optimal spatiotemporal configuration for smoke formation can be calculated, thereby achieving effective optical shielding of the real target. The core of optimizing the deployment strategy lies in coordinating the UAV's flight parameters with the detonation sequence of the flares to maximize the continuity and overlap of shielding coverage during missile approach. In the fields of battlefield environment simulation and tactical research, modeling and analyzing different deployment strategies allows for a deeper understanding of the influence of various parameters on the shielding effect. This research not only helps optimize existing tactical plans but also provides theoretical guidance for the design of new smoke and flare jamming systems. Especially in complex scenarios involving multi-aircraft coordination and multi-missile deployment, system strategy optimization can significantly improve resource utilization efficiency and enhance overall defense effectiveness.

[0005] However, current research on smoke and flare deployment strategies remains significantly insufficient. Existing methods largely focus on single-mode analysis, lacking a systematic theoretical framework for multi-aircraft coordinated deployment of smoke and flares. Therefore, developing a systematic deployment method that can naturally extend from basic single-aircraft, single-flare scenarios to complex multi-aircraft, multi-flare scenarios, and achieve optimized decision-making under different operational scales, has become an urgent need in current military technology research. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and propose an optimal method for the deployment of smoke flares in multi-drone collaborative scenarios. This method provides decision support for multi-UAV collaborative operations, optimizes battlefield resource allocation, improves the adaptability and reliability of smoke flares, and provides key technical support for the development of future intelligent combat systems.

[0007] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0008] S1. Construct the missile trajectory equation based on the known missile trajectory;

[0009] S2. Based on the missile trajectory equation, construct a single-aircraft, single-missile shielding model;

[0010] S3. Solving the maximum effective duration of a single machine and a single missile based on the particle swarm optimization algorithm;

[0011] S4. Based on the single-machine single-missile shielding model, construct a multi-machine joint cooperative shielding model;

[0012] S5. A hybrid optimization algorithm combining particle swarm optimization and genetic algorithm is used to solve the multi-machine joint cooperative masking model. An overlap penalty term is introduced into the optimization objective to minimize the overlap of masking time between multiple smoke clouds. By adjusting the flight parameters of each UAV and the release and detonation sequence of each smoke flare, the global maximum effective masking time is obtained, and the optimal release strategy is determined.

[0013] Preferably, step S1 includes:

[0014] S1-1, Set up false targets to cover real fixed targets A three-dimensional coordinate system is established with the dummy target as the origin;

[0015] S1-2, based on known enemy missiles flight speed and initial position missile The position at any time t is ,missile The missile was flying directly at the decoy target. The specific trajectory equation is expressed as follows:

[0016] (1);

[0017] in, Indicates the angle between the enemy missile's flight trajectory and the horizontal plane. Indicates the azimuth angle of the missile's movement in the horizontal plane;

[0018] Preferably, step S2 includes:

[0019] S2-1, Mission Receiving Phase, Unmanned Aerial Vehicle Smoke screen flares At a constant speed For a drone flying at a constant altitude and speed in a straight line, the equation of motion of the drone and the smoke chaff at time t can be expressed by the following formula:

[0020] (2);

[0021] (3);

[0022] in, For the UAV along each direction of the coordinate axis The initial position, For any time during the mission reception phase, smoke screen flares Location coordinates, The azimuth angle of the drone's movement on the horizontal plane. , The timing of the chaff deployment;

[0023] S2-2, Waiting for Explosion Phase, Smoke Decoy Items With drones Separating and performing projectile motion in space, the smoke chaff is analyzed, and its resultant velocity is decomposed into velocities parallel to the three coordinate axes, as shown below:

[0024] In the vertical direction, with an initial velocity of 0, the object is only subject to gravity.

[0025] (4);

[0026] The vertical movement distance and time of the smoke decoy can be solved using equation (4). Relationship:

[0027] (5);

[0028] Smoke flares The component velocities and equations of motion are solved using the following formulas:

[0029] (6);

[0030] (7);

[0031] in, To wait for any moment of the explosion phase The components of the chaff's velocity parallel to each coordinate axis. Let be the components of the drone's velocity parallel to the coordinate axes. The coordinates of the location at the moment the smoke flares were deployed. For any time in this stage The location coordinates of the chaff. , The timing of chaff deployment. The detonation time of the chaff / flare;

[0032] S2-3, during the smoke screen decoy explosion phase, the cloud formation moves at a speed... The uniform descent can be represented by the following equation of motion:

[0033] The detonation point of the chaff and flares is the initial center point of the smoke cloud. The coordinates of the initial center point can be obtained by the following formula:

[0034] (8);

[0035] The coordinates of the center of the cloud cluster are , ,in, The timing of chaff deployment. For the detonation time of the chaff, The effective time for smoke screen concentration;

[0036] S2-4, Constructing Missiles With real fixed target The equation of the line in space containing the connecting line is expressed as follows:

[0037] Solve for the direction vector of the line using the following formula :

[0038] (9);

[0039] The expression for the spatial parametric equation of the line is obtained as follows:

[0040] (10);

[0041] Further simplification yields:

[0042] (11);

[0043] Solve for the missile using the following formula With the center of the cloud The resulting vector :

[0044] (12);

[0045] Calculate using the vector cross product method have to:

[0046] (13);

[0047] (14);

[0048] Substituting equations (13) and (14) into the following equation, we can obtain the distance from the center of a single smoke cloud to the straight line connecting the enemy missile and the true coordinates. :

[0049] (15);

[0050] S2-5. In a single-missile, single-aircraft scenario, the effectiveness of the smoke cloud in interfering with the missile is determined by comparing the distance d with the cloud radius R. At that time, the cloud formation can interfere with missiles. At that time, the cloud formation could not interfere with the missile, utilizing The precise timing of the missile's entry into and exit from the cloud formation. and The effective shielding time for a single machine and a single missile is calculated as follows:

[0051] (16);

[0052] in, This refers to the effective shielding time for a single machine and a single missile.

[0053] Preferably, step S3 includes:

[0054] S3-1. Construct an optimization model for the effective concealment time of smoke cloud under the single-unit, single-missile scenario. The objective function is to maximize the effective concealment time of the smoke decoy.

[0055] (17);

[0056] in, The directional angle of the drone's movement on the horizontal plane, such as Figure 3 As shown; For the drone's flight speed, These represent the times when the smoke decoy grenades are deployed and when they detonate. These are the decision variables for the objective function;

[0057] Set the constraints as follows:

[0058] The drone's flight speed meets the requirements. The timing of the smoke flare deployment meets the following requirements: Smoke density after detonation The interior can provide effective occlusion for real, fixed targets, that is... The smoke cloud did not land before the missile arrived, meaning... ;

[0059] The optimization model for the effective concealment time of smoke cloud in the case of a single machine and a single missile is as follows:

[0060] (18);

[0061] S3-2. Initialize variables and set parameters, set physical constants, performance parameters of UAV and missile and initial position of scene, and clarify the motion constraint boundary of missile;

[0062] S3-3. Based on the particle swarm optimization algorithm, the speed of a single UAV, flight direction angle, release time of a single smoke chaff and detonation time are used as variables. The optimal solution in the single-UAV single-chaff scenario is searched in three stages: receiving the mission, waiting for the explosion, and the detonation of the chaff, under the premise of meeting the constraints. By setting linearly decreasing weights, the particle positions are adjusted and updated using dynamic weights to balance local optimization and global search.

[0063] S3-4. Calculate the occlusion duration by calling the geometric judgment logic on the candidate solutions, and output the optimal delivery strategy corresponding to the maximum occlusion duration.

[0064] Preferably, step S4 includes:

[0065] S4-1. Using the single-machine, single-missile model in step S3, obtain the distance between the center of the smoke cloud of the i-th decoy missile after its detonation and the straight line connecting the missile and the real fixed target in the multi-machine joint model. :

[0066] (19);

[0067] S4-2. In the case of multi-aircraft collaborative operation, analyze each smoke cloud cluster by comparing distances. The size of the cloud radius R is used to determine whether the smoke cloud effectively interferes with the missile. At that time, the cloud formation can interfere with missiles. At that time, the cloud cluster cannot interfere with the missile. Therefore, the critical time for entering and exiting the cloud cluster can be determined. and The effective concealment time of a single smoke cloud is:

[0068] (20);

[0069] in, The effective concealment time of a single smoke cloud is given. For joint analysis of multiple smoke clouds, without considering overlapping cloud interference, the combined concealment time of the smoke clouds against enemy missiles is:

[0070] (twenty one)

[0071] Preferably, step S5 includes:

[0072] S5-1. Construct an optimization model for the effective concealment time of smoke cloud clusters in a multi-aircraft collaborative scenario. The objective function is to maximize the effective concealment time of smoke decoys.

[0073] (twenty two);

[0074] Establish an optimization model for the effective obscuring time of smoke clouds under a multi-machine collaborative model:

[0075] (twenty three);

[0076] S5-2. For the i-th smoke decoy, the particle swarm optimization algorithm is used to optimize the parameters of each smoke cloud independently. Based on the single-machine single-bomb solution process, the duration of each smoke cloud is accumulated in a time discretization manner. At the same time, the timing arrangement of each decoy is initially considered. By adjusting the deployment and detonation times, the detonation times of each smoke decoy are staggered to avoid premature overlap.

[0077] S5-3. Based on the particle swarm optimization described in step S5-2, a genetic algorithm is used for deep global optimization. While optimizing the duration of single smoke cloud cover, an overlap penalty mechanism is introduced. The time series of multiple smoke decoys are judged by geometric calculation and a penalty weight is set for overlapping periods. At each time point, the effectiveness of smoke cover is verified and the time overlap of multiple smoke clouds is recorded. The release and detonation time of each decoy is adjusted by cross mutation to form a good link between the cover periods.

[0078] S5-4. Make local fine adjustments to the genetic algorithm results to further optimize the timing parameters of each smoke decoy. By fine-tuning the deployment time and detonation time, make the time when the previous smoke cloud fails and the time when the next smoke cloud takes effect as close as possible to minimize the overlap window.

[0079] S5-5. Perform union evaluation and overlap analysis on multiple smoke cloud clusters, convert the effective shading period of each cloud cluster into time intervals, identify the overlapping parts through interval operations, calculate the total effective duration as the union length of the shading intervals of multiple smoke cloud clusters, the effective duration of each cloud cluster as its own interval length, and the overlap duration as the interval intersection length. Compare the union and overlap data before and after optimization to verify the effectiveness of the algorithm parameter adjustment.

[0080] Meanwhile, this invention proposes an optimal deployment system for smoke flares in a multi-machine collaborative scenario, characterized in that the system comprises:

[0081] The missile trajectory construction module is configured to perform the following process: constructing a missile trajectory equation based on the known missile trajectory;

[0082] The single-unit, single-missile shielding model construction module is configured to perform the following process: construct a single-unit, single-missile shielding model based on the missile trajectory equation;

[0083] The single-unit single-missile maximum effective duration optimization module is configured to perform the following process: solve the single-unit single-missile maximum effective duration based on the particle swarm optimization algorithm;

[0084] The multi-machine collaborative occlusion model construction module is configured to perform the following process: construct a multi-machine collaborative occlusion model based on the single-machine single-shot occlusion model;

[0085] The algorithm optimization module is configured to perform the following process: based on a hybrid optimization algorithm combining particle swarm optimization and genetic algorithm, the multi-machine joint cooperative masking model is solved. An overlap penalty term is introduced into the optimization objective to minimize the overlap of masking time between multiple smoke clouds. By adjusting the flight parameters of each UAV and the release and detonation sequence of each smoke flare, the global maximum effective masking time is obtained, and the optimal release strategy is determined.

[0086] Meanwhile, the present invention proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed, it implements the steps of the method described in the present invention.

[0087] Furthermore, the present invention proposes a computer-readable storage medium having a computer program stored thereon, the computer program being configured to implement the steps of the method described in the present invention when invoked by a processor.

[0088] Finally, the present invention proposes a computer program product, including a computer program / instructions, characterized in that the computer program / instructions, when executed by a processor, implement the steps of the method described in the present invention.

[0089] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0090] (1) By constructing a progressive modeling system of “missile trajectory equation - single-machine single missile shielding model - multi-machine joint cooperation model”, the complex multi-machine collaborative problem is decomposed into sub-problems that can be optimized layer by layer, realizing a systematic solution from single-machine local optimum to multi-machine global optimum, which significantly improves the overall shielding duration of multiple smoke cloud clusters in the spatiotemporal dimension.

[0091] (2) The innovative integration of particle swarm optimization and genetic algorithm fully leverages the rapid convergence capability of particle swarm optimization in continuous variable optimization and the global search advantage of genetic algorithm in discrete time-series variable optimization. Under multiple constraints, it collaboratively optimizes the deployment strategy of multiple drones and multiple missile types, solving the problem of insufficient optimization capability of traditional single algorithms in complex collaborative scenarios, and providing efficient and reliable decision support for multi-UAV collaborative interference missions.

[0092] (3) By introducing an overlap penalty mechanism and a time interval union evaluation method in multi-machine joint optimization, the redundancy overlap of multiple smoke cloud clusters in the time dimension is significantly reduced while maximizing the total effective shading time. Thus, the utilization efficiency of shading resources is maximized within the limited smoke resources and effective time. Attached Figure Description

[0093] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0094] Figure 1 This invention relates to a flowchart of an optimal method for deploying smoke flares in a multi-machine collaborative scenario.

[0095] Figure 2 This invention relates to a schematic diagram illustrating the construction process of a single-aircraft, single-projectile shielding model for the optimal deployment method of smoke flares in a multi-aircraft collaborative scenario.

[0096] Figure 3 This invention relates to a schematic diagram of the UAV's operating direction angle for an optimal method of deploying smoke flares in a multi-aircraft collaborative scenario.

[0097] Figure 4 This invention relates to a schematic diagram of the velocity decomposition of smoke decoy grenades during the detonation phase, which illustrates the optimal deployment method of smoke decoy grenades in a multi-machine collaborative scenario.

[0098] Figure 5 This is a schematic diagram illustrating the effective shielding of smoke clouds in the case of a single machine and a single missile in Example 1.

[0099] Figure 6 This is a schematic diagram of the effective shielding cross-section of the smoke cloud in the case of a single machine and a single missile in Example 1.

[0100] Figure 7 This is a schematic diagram illustrating the ineffective obscuring of smoke clouds in the case of a single machine and single missile in Example 1.

[0101] Figure 8 This is a schematic diagram of the cross-section of the smoke cloud ineffectively shielding a single aircraft and single missile in Example 1.

[0102] Figure 9 This is a trajectory diagram of the center of an incoming missile, drone, smoke decoy, and smoke cloud in the case of a single aircraft and single missile in Example 1.

[0103] Figure 10 This is a graph showing the effect of the UAV's heading angle on the total shielding time in the case of a single UAV and a single missile in Example 1.

[0104] Figure 11 This is a graph showing the effect of UAV flight speed on total cover time in the case of a single UAV and a single missile in Example 1.

[0105] Figure 12 This is a graph showing the effect of the timing of smoke decoy grenade deployment on the total duration of obfuscation in the case of a single machine and single missile in Example 1.

[0106] Figure 13 This is a graph showing the effect of the detonation time of the smoke decoy grenade on the total shielding time in the case of a single machine and single bomb in Example 1.

[0107] Figure 14 This is a schematic diagram illustrating the effective and ineffective shielding of the smoke cloud in the case of three aircraft and three missiles in Example 2.

[0108] Figure 15 This is a schematic diagram of the effective and ineffective shielding of the smoke cloud in the case of three aircraft and three missiles in Example 2.

[0109] Figure 16 This is a schematic diagram of smoke cloud obscuring in the case of three aircraft and three missiles in Example 2.

[0110] Figure 17 This is a schematic diagram of the cross-section of the smoke cloud obscuring the scene in Example 2, where there are three aircraft and three missiles.

[0111] Figure 18 Example 2: Missiles and UAVs Smoke screen flares Trajectory diagram.

[0112] Figure 19 Example 2: Missiles and UAVs Smoke screen flares Trajectory diagram.

[0113] Figure 20 Example 2: Missiles and UAVs Smoke screen flares Trajectory diagram.

[0114] Figure 21 This is a graph showing the relationship between the heading angle of each UAV and the total duration of cover in the case of three UAVs and three missiles in Example 2.

[0115] Figure 22 This is a graph showing the relationship between the flight speed of each UAV and the total duration of cover in the case of three UAVs and three missiles in Example 2.

[0116] Figure 23 This is a graph showing the relationship between the deployment time of each smoke grenade and the total duration of obfuscation in the case of three aircraft and three missiles in Example 2.

[0117] Figure 24 This is a graph showing the relationship between the detonation time of each smoke decoy and the total duration of obfuscation in the case of three machines and three missiles in Example 2.

[0118] Figure 25 This is a schematic diagram of the effective overlapping area of ​​each smoke cloud in the case of three aircraft and three missiles in Example 2. Detailed Implementation

[0119] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. Of course, the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0120] Example 1: As Figure 1 As shown, the overall implementation of the optimal deployment method for smoke flares in a multi-aircraft collaborative scenario, as disclosed in this invention, includes the following steps:

[0121] S1. Construct the missile trajectory equation based on the known missile trajectory;

[0122] S1.1, intended to cover up the real fixed target A three-dimensional coordinate system is established with a specially designed dummy target as the origin.

[0123] S1.2, Based on known enemy missiles flight speed and initial position missile at any time The position is Assuming the missile's flight direction is directly aimed at the decoy target, its specific trajectory equation is expressed as follows:

[0124]

[0125] in, Indicates the angle between the enemy missile's flight trajectory and the horizontal plane. Indicates the azimuth angle of the missile's movement in the horizontal plane;

[0126] S2. Based on the missile trajectory equation, construct a single-aircraft, single-missile shielding model, the process of which is as follows: Figure 2 As shown;

[0127] S2.1 Assume the drone travels at a constant speed. Constant altitude, uniform speed, straight flight, mission receiving phase ( Drones Smoke screen flares Flying together, then The equations of motion for both at time t are expressed as follows:

[0128]

[0129]

[0130] in, For the drone parallel to each direction of the coordinate axis , The initial position, For any time during the mission reception phase, smoke screen flares Location coordinates, The azimuth angle of the drone's movement on the horizontal plane;

[0131] S2.2, Waiting for Explosion Stage ( Smoke screen flares With drones They separate and undergo projectile motion in space, where, The timing of chaff deployment. This refers to the detonation time of the decoy flare. (Reference) Figure 4 Analyzing the smoke grenade, its resultant velocity is decomposed into velocities parallel to the three coordinate axes, as shown below:

[0132] In the vertical direction, with an initial velocity of 0, the object is only subject to gravity.

[0133]

[0134] Therefore, the relationship between the vertical distance traveled and time can be obtained:

[0135]

[0136] Decipher the smoke screen chaff The equations of motion for each component velocity are as follows:

[0137]

[0138]

[0139] in, To wait for any moment of the explosion phase The components of the chaff's velocity parallel to each coordinate axis. Let be the components of the drone's velocity parallel to the coordinate axes. The coordinates of the location at the moment the smoke flares were deployed. For any time in this stage The location coordinates of the chaff;

[0140] S2.3, Smoke screen decoy explosion stage ( Assuming the cloud cluster travels at a speed of... It sinks at a constant speed, among which, The timing of chaff deployment. For the detonation time of the chaff, The effective time for smoke screen concentration. The specific equation of motion is as follows:

[0141] The explosion point of the chaff and flares is considered the initial center point of the smoke cloud. The coordinates of the initial center point are obtained as follows:

[0142]

[0143] The coordinates of the center of the cloud cluster are as follows: ;

[0144] S2.4, Constructing the missile With true target The equation of the line in space containing the connecting line is expressed as follows:

[0145] Solving for the direction vector of the line :

[0146]

[0147] The expression for the spatial parametric equation of the line is obtained as follows:

[0148]

[0149] Further simplification yields:

[0150]

[0151] Solve for missile With the center of the cloud The resulting vector :

[0152]

[0153] Calculate using the vector cross product method have to:

[0154]

[0155]

[0156] Substituting the above two equations into the calculation, we can obtain the distance from the center of a single smoke cloud to the straight line connecting the enemy missile and the true coordinates. for:

[0157]

[0158] S2.5. In the case of a single missile and a single aircraft, the effectiveness of the smoke cloud in interfering with the missile is determined by comparing the distance d with the radius R of the smoke cloud. At that time, the cloud formation can interfere with missiles. At that time, the cloud formation cannot interfere with the missile. Utilizing The precise timing of the missile's entry into and exit from the cloud formation. and The effective shielding time for a single machine and a single missile is calculated as follows:

[0159]

[0160] in, The effective shielding time for a single machine and a single missile;

[0161] S3. Solving the maximum effective duration of a single machine and a single missile based on the particle swarm optimization algorithm;

[0162] S3.1 Construct an optimization model for the effective obscuring time of smoke cloud clusters under the single-unit, single-missile scenario, as detailed below:

[0163] With the goal of maximizing the effective concealment time of smoke flares, an objective function is established:

[0164]

[0165] in, The azimuth angle of the drone's movement on the horizontal plane. For the drone's flight speed, These represent the times when the smoke decoy grenades are deployed and when they detonate. These are the decision variables for the objective function;

[0166] Set the constraints as follows:

[0167] The drone is required to fly within a certain speed range, that is Smoke flares are deployed before enemy missiles hit decoy targets, i.e. The regulations stipulate that the smoke density after detonation must be within a certain range. The interior can provide effective occlusion for the real target, that is... The smoke cloud did not land before the missile arrived, meaning... .

[0168] In summary, the optimization model for the effective concealment time of the smoke cloud in the case of a single aircraft and a single missile is as follows:

[0169]

[0170] S3.2 Initialize variables and set parameters: Set physical constants, performance parameters of UAV and missile, and initial position of the scene, and define the motion constraint boundary of the missile;

[0171] S3.3 Based on the particle swarm optimization algorithm, the speed of a single UAV, flight direction angle, release time of a single smoke chaff and detonation time are used as variables. The optimal solution in the single-UAV single-chaff scenario is searched in three stages: receiving the mission, waiting for the explosion, and the detonation of the chaff, under the premise of meeting the constraints. By setting linearly decreasing weights, the particle positions are adjusted and updated using dynamic weights to balance local optimization and global search.

[0172] S3.4. Call the geometric judgment logic to calculate the occlusion duration for the candidate solutions, and output the optimal delivery strategy corresponding to the maximum occlusion duration.

[0173] S4. Based on the single-machine single-missile shielding model, construct a three-machine joint cooperative shielding model;

[0174] S4.1. From the single-machine, single-missile model in S3, obtain the distance between the center of the smoke cloud of the i-th decoy missile after its detonation and the straight line connecting the missile and the real target in the three-machine, three-missile model. for:

[0175]

[0176] S4.2 In the case of three machines working together, analyze each smoke cloud cluster by comparing distances. The size of the cloud radius R is used to determine whether the smoke cloud effectively interferes with the missile. At that time, the cloud formation can interfere with missiles. At that time, the cloud cluster cannot interfere with the missile. Therefore, the critical time for entering and exiting the cloud cluster can be determined. and The effective concealment time of a single smoke cloud is:

[0177]

[0178] in, The effective concealment time of a single smoke cloud;

[0179] A joint analysis of the three smoke cloud formations, without considering the interference of overlapping cloud formations, shows that the combined duration of the smoke cloud formations' coverage of enemy missiles is:

[0180]

[0181] S5. A hybrid optimization algorithm combining particle swarm optimization and genetic algorithm is used to solve the multi-machine joint cooperative masking model. An overlap penalty term is introduced into the optimization objective to minimize the overlap of masking time between multiple smoke clouds. By adjusting the flight parameters of each UAV and the release and detonation sequence of each smoke flare, the global maximum effective masking time is obtained, and the optimal release strategy is determined.

[0182] S5.1 Construct an optimization model for the effective shading time of smoke clouds in the case of three-machine joint cooperation, as specifically expressed below:

[0183] With the goal of maximizing the effective concealment time of smoke flares, an objective function is established:

[0184]

[0185] With all constraints remaining the same, the optimization model for the effective shading time of the smoke cloud under the three-machine joint cooperation model is shown below:

[0186]

[0187] S5.2 For the i-th projectile, the particle swarm optimization algorithm is used to optimize the parameters of each smoke cloud independently. Based on the single-machine single-projectile solution process, the duration of each smoke cloud is accumulated in a time discretization manner. At the same time, the timing arrangement of each jamming projectile is initially considered. By adjusting the deployment and detonation times, the detonation times of each smoke jamming projectile are staggered to avoid premature overlap.

[0188] S5.3. Based on the particle swarm optimization results in S5.2, a genetic algorithm is used for deep global optimization. While optimizing the duration of single smoke cloud cover, an overlap penalty mechanism is introduced. The time series of multiple smoke decoys are judged through geometric calculations, and penalty weights are set for overlapping periods. At each time point, the effectiveness of smoke cover is verified, and the time overlap of multiple smoke clouds is recorded. The deployment and detonation times of each decoy are adjusted through cross-mutation to ensure a good link between the cover periods.

[0189] S5.4. Fine-tune the genetic algorithm results locally to further optimize the timing parameters of each smoke decoy. By fine-tuning the deployment and detonation times, the time when the previous smoke cloud fails is as close as possible to the time when the next smoke cloud takes effect, minimizing the overlap window.

[0190] S5.5. Perform union evaluation and overlap analysis on multiple smoke cloud clusters. Convert the effective shading period of each cloud cluster into time intervals. Identify overlapping parts through interval operations. Calculate the total effective duration as the union length of the shading intervals of the multiple smoke cloud clusters, the effective duration of each cloud cluster as its own interval length, and the overlap duration as the intersection length of the intervals. Compare the union and overlap data before and after optimization to verify the effectiveness of the algorithm parameter adjustment.

[0191] To verify the effectiveness of the present invention, numerical simulation was performed using Python.

[0192] Specifically, a drone is used to deploy a smoke grenade to interfere with an incoming missile. By optimizing the flight direction and speed of drone FY1, as well as the timing of the smoke grenade's deployment and detonation, the effective shielding time is maximized. In the scenario, the missile's initial position is... ), with 300 Flying in a straight line towards the false target at high speed True target T coordinates Keep fixed; the initial position of UAV FY1 is fixed at... Flight speed is limited to Within the range, smoke flares dropped from drones will fall freely under gravity, and the resulting smoke cloud will... The smoke screen descends at a speed of [speed], and its effective radius is [radius]. The effective shielding duration is In order to effectively shield the missile, the explosion must be completed before the smoke screen lands.

[0193] The geometric and motion relationships in the scene, as well as the occlusion criteria, are clearly presented through multiple diagrams: Figure 5 , 6Figures 7 and 8 show the effective shielding space, effective shielding cross-section, and ineffective shielding space and cross-section of the smoke cloud in a one-missile-one-aircraft scenario, respectively. The vertical distance from the center of the smoke cloud to the line segment connecting the missile and the real target is also shown. When the smoke screen completely obscures the missile's line of sight, it is considered an effective shielding; when the vertical distance is greater than 10m, the line of sight is not completely obscured, and it is considered an ineffective shielding.

[0194] The core optimization variables are extracted: UAV speed, flight direction angle, smoke decoy deployment time, and detonation time. A particle swarm optimization algorithm is used to optimize in three stages: task acceptance, waiting for detonation, and decoy detonation. Under constraints such as the UAV speed range and smoke detonation time window, dynamic weights decreasing linearly from 0.9 to 0.4 are used to update particle positions, balancing global search and local optimization. For candidate solutions generated in the iteration, the vertical distance from the smoke cloud center to the line segment connecting the missile and the real target is used. The geometric judgment logic accumulates the effective occlusion duration and finally selects the optimal strategy corresponding to the maximum occlusion duration.

[0195] Figure 10 , 11 Figures 12 and 13 respectively present the relationship characteristics between heading angle, flight speed, release time, detonation time, and total shielding duration: When heading angle, flight speed, and detonation time are single variables, the shielding time follows a downward-opening parabolic distribution. There exists an optimal heading angle, optimal speed, and optimal detonation time that maximizes the total shielding duration. Within a specific range, the shielding time can be maintained above a threshold. Deviating from this range, the rate of change of shielding time increases significantly, and the shielding duration decreases sharply. The impact of release time on shielding effectiveness shows different patterns: the shielding time is maximized when the release time is 0 seconds, and then decreases rapidly with time delay. The comprehensive analysis of the four figures shows that the four variables do not act independently on the optimization of shielding time, but rather have a synergistic relationship. The optimal effective shielding duration can only be obtained through multi-parameter joint optimization.

[0196] The simulated drone flew with optimal parameters and dropped smoke grenades. Combining schematic diagrams of various motion trajectories and shielding states, the missile position and the downward movement of the smoke cloud were calculated in real time. The shielding state was continuously assessed to confirm the rationality of the parameters and the stability of the shielding effect. The final optimization results are shown in Table 1. Figure 9 The trajectory map shows the center of the incoming missiles, drones, smoke screen flares, and smoke cloud formations.

[0197] Table 1

[0198]

[0199] Verification has shown that the same optimization logic and verification method were used to test different combinations of parameters in different scenarios. The verification results for other scenarios were also consistent. Therefore, the UAV smoke grenade deployment strategy optimization method based on particle swarm optimization proposed in this invention is truly effective in the case of a single UAV and a single grenade, laying the foundation for subsequent research on multi-UAV and multi-grenade jamming strategies.

[0200] Subsequently, through optimization The flight parameters of the three drones, along with the deployment and detonation parameters of each drone's smoke screen flare, achieve the longest possible coordinated shielding against the incoming missile. In this scenario, the missile's initial position remains... Flying in a straight line towards the false target Flight speed is 300 The initial positions of the three drones were as follows: , , The flight speed of a single drone is limited to Within the range, smoke flares dropped from drones will fall freely under gravity, and the resulting smoke cloud will... The smoke screen descends at a speed of [speed], and its effective radius is [radius]. The effective shielding duration is In order to effectively shield the missile, the explosion must be completed before the smoke screen lands.

[0201] The geometric and motion relationships in the scene, as well as the occlusion criteria, are clearly presented through multiple diagrams: Figure 14 , 15 Figures 16 and 17 show the effective and ineffective shielding space, effective and ineffective shielding cross-section, shielding space, and shielding cross-section of the smoke cloud in a three-aircraft, three-missile scenario, respectively. The vertical distance from the center of the smoke cloud to the line segment connecting the missile and the real target is also shown. When the smoke screen completely obscures the missile's line of sight, it is considered an effective shielding; when the vertical distance is greater than 10m, the line of sight is not completely obscured, and it is considered an ineffective shielding.

[0202] In optimizing a single smoke screen, a three-stage optimization strategy is employed, sequentially performing particle swarm optimization (PSO), genetic algorithm optimization, and local fine-tuning. First, PSO is used for initial searching, employing a greedy strategy combined with random perturbation to generate diverse initial solutions. During iteration, particle velocity is adjusted to update positions, aiming to find a better solution with the single-smoke screen occlusion duration as the objective. Next, the optimal solution obtained from PSO, along with some high-quality seeds, is used as the initial population for genetic algorithm optimization. Tournament selection, interval expansion crossover, and Gaussian perturbation mutation operations further improve the occlusion effect. Finally, local fine-tuning is performed on the optimal solution obtained from the genetic algorithm, refining each parameter through a neighborhood search with progressively smaller step sizes.

[0203] The final optimization results are shown in Table 2. Figure 18 , 19 Figures 2 and 20 show the trajectories of UAVs FY1, FY2, and FY3, as well as their respective smoke decoys, under optimal conditions. The figures clearly show that under specific parameters, the smoke cloud of each UAV can accurately cover the critical period of the missile's flight path. Figure 21 , 22 Figures 23 and 24 respectively present the relationship characteristics between heading angle, flight speed, deployment time, detonation time, and total shielding duration: As can be seen from the figures, different UAVs have their own optimal ranges for heading angle and flight speed, which can maximize the shielding duration; the deployment time and detonation time of the smoke grenade have a significant impact on the shielding duration, and there are specific times when the cloud shielding effect is optimal; all four variables have a significant impact on the shielding duration, and the three UAVs cannot be in the same state. Each of these four variables has its own optimal value, and for the same UAV or the same smoke grenade, the four variables will have the optimal value for effective shielding duration. Figure 25 The diagram illustrates the effective coverage overlap of each smoke cloud. The image visually presents the effective coverage periods and overlap of the three smoke grenades. It further demonstrates that by coordinating and optimizing these variables, the coverage periods of multiple smoke grenades can be reasonably connected and partially overlapped. This avoids long gaps of no coverage after the coverage of a single smoke grenade ends, and enhances the coverage effect during critical periods through overlap, thereby significantly extending the total effective coverage time for incoming missiles.

[0204] Table 2

[0205]

[0206] The meanings of the symbols in this embodiment are shown in Table 3:

[0207] Table 3

[0208]

[0209] Verification showed that the same optimization logic and verification method were used to test different combinations of scenario parameters, and the verification results for other scenarios were also the same. Therefore, the optimal deployment method of smoke chaff in multi-machine joint cooperation scenarios based on particle swarm-genetic fusion algorithm proposed in this invention is real and effective, and provides support for the optimization and iteration of subsequent chaff deployment schemes and practical applications.

[0210] Example 2: This example proposes an optimal deployment system for smoke flares in a multi-machine collaborative scenario, including:

[0211] The missile trajectory construction module is configured to perform the following process: constructing a missile trajectory equation based on the known missile trajectory;

[0212] The single-unit, single-missile shielding model construction module is configured to perform the following process: construct a single-unit, single-missile shielding model based on the missile trajectory equation;

[0213] The single-unit single-missile maximum effective duration optimization module is configured to perform the following process: solve the single-unit single-missile maximum effective duration based on the particle swarm optimization algorithm;

[0214] The multi-machine collaborative occlusion model construction module is configured to perform the following process: construct a multi-machine collaborative occlusion model based on the single-machine single-shot occlusion model;

[0215] The algorithm optimization module is configured to perform the following process: based on a hybrid optimization algorithm combining particle swarm optimization and genetic algorithm, the multi-machine joint cooperative masking model is solved. An overlap penalty term is introduced into the optimization objective to minimize the overlap of masking time between multiple smoke clouds. By adjusting the flight parameters of each UAV and the release and detonation sequence of each smoke flare, the global maximum effective masking time is obtained, and the optimal release strategy is determined.

[0216] Example 3: This example proposes an electronic system, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method steps of the present invention.

[0217] Example 4: This example proposes a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the steps of the method described in this invention, which will not be repeated here.

[0218] Example 5: This example proposes a computer program product, including a computer program / instructions. When the computer program / instructions are executed by a processor, they implement the steps of the method described in this invention, which will not be repeated here.

[0219] It should be noted that the processing flow of embodiments 2-5 corresponds to the specific steps of the method provided in embodiment 1 of the present invention, and has the corresponding functional modules and beneficial effects of the method. Technical details not described in detail in this embodiment can be found in the method provided in embodiment 1 of the present invention.

[0220] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0221] The specific implementation schemes described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific implementation schemes of the present invention and are not intended to limit the scope of the present invention. Any equivalent changes and modifications made by those skilled in the art without departing from the concept and principles of the present invention should fall within the scope of protection of the present invention.

Claims

1. An optimal method for deploying smoke flares in a multi-aircraft collaborative scenario, characterized in that, Includes the following steps: S1. Construct the missile trajectory equation based on the known missile trajectory; S2. Based on the missile trajectory equation, construct a single-aircraft, single-missile shielding model; S3. Solving the maximum effective duration of a single machine and a single missile based on the particle swarm optimization algorithm; S4. Based on the single-machine single-missile shielding model, construct a multi-machine joint cooperative shielding model; S5. A hybrid optimization algorithm combining particle swarm optimization and genetic algorithm is used to solve the multi-machine joint cooperative masking model. An overlap penalty term is introduced into the optimization objective to minimize the overlap of masking time between multiple smoke clouds. By adjusting the flight parameters of each UAV and the release and detonation sequence of each smoke flare, the global maximum effective masking time is obtained, and the optimal release strategy is determined.

2. The optimal deployment method for smoke flares in a multi-aircraft collaborative scenario according to claim 1, characterized in that, Step S1 includes: S1-1, Set up false targets to cover real fixed targets A three-dimensional coordinate system is established with the dummy target as the origin; S1-2, based on known enemy missiles flight speed and initial position missile The position at any time t is ,missile The missile was flying directly at the decoy target. The specific trajectory equation is expressed as follows: （1）； in, Indicates the angle between the enemy missile's flight trajectory and the horizontal plane. This indicates the azimuth angle of the missile's movement in the horizontal plane.

3. The optimal deployment method for smoke flares in a multi-aircraft collaborative scenario according to claim 2, characterized in that, Step S2 includes: S2-1, Mission Receiving Phase, Unmanned Aerial Vehicle Smoke screen flares At a constant speed For a drone flying at a constant altitude and speed in a straight line, the equation of motion of the drone and the smoke chaff at time t can be expressed by the following formula: （2）； （3）； in, Let be the velocity components of the UAV along each coordinate axis. The initial position, For any time during the mission reception phase, smoke screen flares Location coordinates, The azimuth angle of the drone's movement on the horizontal plane. , The timing of the chaff deployment; S2-2, Waiting for Explosion Phase, Smoke Decoy Items With drones Separating and performing projectile motion in space, the smoke chaff is analyzed, and its resultant velocity is decomposed into velocities parallel to the three coordinate axes, as shown below: In the vertical direction, with an initial velocity of 0, the object is only subject to gravity. （4）； The vertical movement distance and time of the smoke decoy can be solved using equation (4). Relationship: （5）； Smoke flares The component velocities and equations of motion are solved using the following formulas: （6）； （7）； in, To wait for any moment of the explosion phase The components of the chaff's velocity parallel to each coordinate axis. Let be the components of the drone's velocity parallel to the coordinate axes. The coordinates of the location at the moment the smoke flares were deployed. For any time in this stage The location coordinates of the chaff. , The timing of chaff deployment. The detonation time of the chaff / flare; S2-3, during the smoke screen decoy explosion phase, the cloud formation moves at a speed... The uniform descent can be represented by the following equation of motion: The detonation point of the chaff and flares is the initial center point of the smoke cloud. The coordinates of the initial center point can be obtained by the following formula: （8）； The coordinates of the center of the cloud cluster are , ,in, The timing of chaff deployment. For the detonation time of the chaff, The effective time for smoke screen concentration; S2-4, Constructing Missiles With real fixed target The equation of the line in space containing the connecting line is expressed as follows: Solve for the direction vector of the line using the following formula : （9）； The expression for the spatial parametric equation of the line is obtained as follows: （10）； Further simplification yields: （11）； Solve for the missile using the following formula With the center of the cloud The resulting vector : （12）； Calculate using the vector cross product method have to: （13）； （14）； Substituting equations (13) and (14) into the following equation, we can obtain the distance from the center of a single smoke cloud to the straight line connecting the enemy missile and the true coordinates. : （15）； S2-5. In a single-missile, single-aircraft scenario, the effectiveness of the smoke cloud in interfering with the missile is determined by comparing the distance d with the cloud radius R. At that time, the cloud formation can interfere with missiles. At that time, the cloud formation could not interfere with the missile, utilizing The precise timing of the missile's entry into and exit from the cloud formation. and The effective shielding time for a single machine and a single missile is calculated as follows: （16）； in, This refers to the effective shielding time for a single machine and a single missile.

4. The optimal deployment method for smoke flares in a multi-machine collaborative scenario according to claim 3, characterized in that, Step S3 includes: S3-1. Construct an optimization model for the effective concealment time of smoke cloud under the single-unit, single-missile scenario. The objective function is to maximize the effective concealment time of the smoke decoy. （17）； in, The azimuth angle of the drone's movement on the horizontal plane. For the drone's flight speed, These represent the times when the smoke decoy grenades are deployed and when they detonate. These are the decision variables for the objective function; Set the constraints as follows: The drone's flight speed meets the requirements. The timing of the smoke flare deployment meets the following requirements: Smoke density after detonation The interior can provide effective occlusion for real, fixed targets, that is... The smoke cloud did not land before the missile arrived, meaning... ; The optimization model for the effective concealment time of smoke cloud in the case of a single machine and a single missile is as follows: （18）； S3-2. Initialize variables and set parameters, set physical constants, performance parameters of UAV and missile and initial position of scene, and clarify the motion constraint boundary of missile; S3-3. Based on the particle swarm optimization algorithm, the speed of a single UAV, flight direction angle, release time of a single smoke chaff and detonation time are used as variables. The optimal solution in the single-UAV single-chaff scenario is searched in three stages: receiving the mission, waiting for the explosion, and the detonation of the chaff, under the premise of meeting the constraints. By setting linearly decreasing weights, the particle positions are adjusted and updated using dynamic weights to balance local optimization and global search. S3-4. Calculate the occlusion duration by calling the geometric judgment logic on the candidate solutions, and output the optimal delivery strategy corresponding to the maximum occlusion duration.

5. The optimal deployment method for smoke flares in a multi-aircraft cooperative scenario according to claim 4, characterized in that, Step S4 includes: S4-1. Using the single-machine, single-missile model in step S3, obtain the distance between the center of the smoke cloud of the i-th decoy missile after its detonation and the straight line connecting the missile and the real fixed target in the multi-machine joint model. : （19）； S4-2. In the case of multi-aircraft collaborative operation, analyze each smoke cloud cluster by comparing distances. The size of the cloud radius R is used to determine whether the smoke cloud effectively interferes with the missile; when At that time, the cloud formation can interfere with missiles. At that time, the cloud cannot interfere with the missile; therefore, the critical time for entering and exiting the cloud can be determined. and The effective concealment time of a single smoke cloud is: （20）； in, The effective concealment time of a single smoke cloud is given. For joint analysis of multiple smoke clouds, without considering overlapping cloud interference, the combined concealment time of the smoke clouds against enemy missiles is: （21）。 6. The optimal deployment method for smoke flares in a multi-aircraft cooperative scenario according to claim 5, characterized in that, Step S5 includes: S5-1. Construct an optimization model for the effective concealment time of smoke cloud clusters in a multi-aircraft collaborative scenario. The objective function is to maximize the effective concealment time of smoke decoys. （22）； Establish an optimization model for the effective obscuring time of smoke clouds under a multi-machine collaborative model: （23）； S5-2. For the i-th smoke decoy, the particle swarm optimization algorithm is used to optimize the parameters of each smoke cloud independently. Based on the single-machine single-bomb solution process, the duration of each smoke cloud is accumulated in a time discretization manner. At the same time, the timing arrangement of each decoy is initially considered. By adjusting the deployment and detonation times, the detonation times of each smoke decoy are staggered to avoid premature overlap. S5-3. Based on the particle swarm optimization described in step S5-2, a genetic algorithm is used for deep global optimization. While optimizing the duration of single smoke cloud cover, an overlap penalty mechanism is introduced. The time series of multiple smoke decoys are judged by geometric calculation and a penalty weight is set for overlapping periods. At each time point, the effectiveness of smoke cover is verified and the time overlap of multiple smoke clouds is recorded. The release and detonation time of each decoy is adjusted by cross mutation to form a good link between the cover periods. S5-4. Make local fine adjustments to the genetic algorithm results to further optimize the timing parameters of each smoke decoy. By fine-tuning the deployment time and detonation time, make the time when the previous smoke cloud fails and the time when the next smoke cloud takes effect as close as possible to minimize the overlap window. S5-5. Perform union evaluation and overlap analysis on multiple smoke cloud clusters, convert the effective shading period of each cloud cluster into time intervals, identify the overlapping parts through interval operations, calculate the total effective duration as the union length of the shading intervals of multiple smoke cloud clusters, the effective duration of each cloud cluster as its own interval length, and the overlap duration as the interval intersection length. Compare the union and overlap data before and after optimization to verify the effectiveness of the algorithm parameter adjustment.

7. An optimal deployment system for smoke flares in a multi-machine collaborative scenario, characterized in that, The system includes: The missile trajectory construction module is configured to perform the following process: constructing a missile trajectory equation based on the known missile trajectory; The single-unit, single-missile shielding model construction module is configured to perform the following process: construct a single-unit, single-missile shielding model based on the missile trajectory equation; The single-unit single-missile maximum effective duration optimization module is configured to perform the following process: solve the single-unit single-missile maximum effective duration based on the particle swarm optimization algorithm; The multi-machine collaborative occlusion model construction module is configured to perform the following process: construct a multi-machine collaborative occlusion model based on the single-machine single-shot occlusion model; The algorithm optimization module is configured to perform the following process: based on a hybrid optimization algorithm combining particle swarm optimization and genetic algorithm, the multi-machine joint cooperative masking model is solved. An overlap penalty term is introduced into the optimization objective to minimize the overlap of masking time between multiple smoke clouds. By adjusting the flight parameters of each UAV and the release and detonation sequence of each smoke flare, the global maximum effective masking time is obtained, and the optimal release strategy is determined.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed, it implements the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is configured to implement the steps of the method according to any one of claims 1 to 6 when invoked by a processor.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 6.

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