A method, system, medium and program product for simulating disturbance impulse injection in a drone network system

Abstract

The present invention relates to the field of drone cluster elastic control technology, and in particular to a method, system, medium, and program product for simulating disturbance impulse injection in a drone network system. The method comprises setting injection rounds, generating an interference parameter matrix, initializing a network model, injecting interference round by round, recording phase point trajectories, determining recovery based on termination conditions, saving standardized trajectory data, and automatically identifying transition zones in the trajectory. The supporting system automates interference injection, trajectory monitoring, recovery determination, and data segmentation processing, and is capable of outputting key indicators such as maximum offset and recovery time. The invention has the advantages of standardized test structure, unified data format, strong adaptability, and high efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) cluster elastic control, and in particular to a method, system, medium and program product for simulating disturbance impulse injection in a UAV network system. Background Art

[0002] In recent years, homogeneous network systems composed of multiple autonomous unmanned aerial vehicles (UAVs) have been widely used in scenarios such as disaster reconnaissance, environmental monitoring, material delivery, and formation performances. Unlike traditional single-machine control, UAV network systems rely on real-time communication and collaborative decision-making between nodes to complete group tasks. Their overall performance depends not only on the maneuverability of individual machines but also on the coupled dynamic behavior and robustness of the network. During actual operation, random factors such as communication interruptions, node failures, sudden wind events, and external electromagnetic interference can act on network nodes in the form of "transient impulses," causing phase points (or state points) to deviate from the intended trajectory. If the system lacks a rapid recovery mechanism, mission failure can occur at best, or even cluster collapse or even safety incidents can occur at worst.

[0003] To analyze the risks brought by such disturbances, academia and industry have proposed several assessment methods. Existing technologies can be roughly divided into three categories:

[0004] 1. Analytical stability evaluation

[0005] Researchers typically abstract drone networks into a system of coupled differential equations and use Lyapunov theory or spectral radius criteria to derive local stability conditions for the system under small signals. These methods can provide closed-form invariant sets or convergence regions, but they assume that the disturbance amplitude approaches zero infinitely. For impulse disturbances of finite amplitude and variable duration, analytical methods struggle to simultaneously cover the two-dimensional space of intensity and duration, and are unable to quantify metrics such as "maximum offset" and "recovery time" during nonlinear recovery.

[0006] 2. Continuous perturbation sweep simulation

[0007] Another approach involves applying continuous periodic perturbations (such as sinusoidal or random noise) during discrete-time simulation, recording the system response and calculating the steady-state error or amplitude amplification factor. While this method can reflect certain dynamic characteristics, it primarily targets continuous inputs; sudden impulses often change the system's momentum in a very short time before rapidly dissipating. Continuous sweeps cannot accurately capture the typical "instantaneous injection-withdrawal-recovery" process, nor can they produce comparable data directly corresponding to the impulse amplitude and action step length.

[0008] 3. Monte Carlo randomized experiment

[0009] Some industrial tests directly perform a large number of Monte Carlo simulations offline: random perturbations are applied to random nodes at random times to observe whether the system ultimately stabilizes. Although this strategy has wide coverage, it has three shortcomings:

[0010] 1) Low efficiency: To ensure statistical significance, thousands or even tens of thousands of simulations must be run, which consumes a huge amount of time and computing power;

[0011] 2) Data organization is chaotic: the lack of a unified trajectory format and segmentation processing rules makes quantitative comparison difficult;

[0012] 3) Lack of limit constraints: Random values ​​may exceed the system's tolerance, causing simulation to diverge early, wasting resources and making it difficult to extract boundary information. Summary of the Invention

[0013] In order to solve the above technical problems, the purpose of the present invention is to provide a disturbance impulse injection simulation method for a UAV network system. This method establishes a two-dimensional interference parameter matrix, designs a unified termination criterion, saves the standardized trajectory matrix and automatically executes transition zone segmentation. It can not only ensure that each round of injection is based on the same stable domain and ensures that the impulse data meets the limit constraints, but also output a directly comparable "maximum deviation-recovery time" indicator, thereby providing a quantitative basis for the controller design, fault tolerance mechanism optimization and mission safety margin analysis of the UAV network.

[0014] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions:

[0015] A method for simulating disturbance impulse injection in a UAV network system, the method comprising the following steps:

[0016] 1) Set the number of injections: preset a positive integer cnt, which represents the number of interference injection rounds to be performed in this experiment;

[0017] 2) Generate interference parameter matrix: Construct a two-dimensional numerical matrix D σ , where the first row is the interference intensity of each round, and the second row is the corresponding action step length;

[0018] 3) Prepare the network system model: Fix the initial state of the network system model, start the model, and let it run to a stable state; ensure that the network system model remains in the same stable state each time interference is injected;

[0019] 4) Inject the value of the i-th column of the disturbance intensity data matrix: Set the disturbance intensity to σ in the disturbance impulse injection module of the model m , the action duration is sp m , so that the simulation model continues to run;

[0020] 5) Record and observe phase point trajectory data: Monitor the changes of phase points in the phase vector space in real time in the phase point trajectory observation window; when it is observed that the change of phase point position is in the continuous sp end less than the termination threshold ε within the step size end , terminate the network operation and go to step 6); otherwise, continue to execute step 5);

[0021] 6) Save the phase point trajectory data of the mth injection experiment: Save the phase point trajectory data of the ith perturbation impulse injection experiment as (n+1)×spr m The numerical matrix D trcm , where spr m is the number of simulation steps from the time the disturbance is injected to the time it is fully recovered to the stable operating state;

[0022] 7) Obtain the experimental data of disturbance impulse injection: inject cnt disturbance impulses into all the numerical matrices D obtained from the experiment trcm (m=1,…, cnt) is saved as a cell array D eo ;

[0023] 8) Segment processing: inject experimental data D into the disturbance impulse eo Segmented processing is performed to find the transition region of the phase point trajectory corresponding to each numerical matrix in the cell array. After the phase point passes through the transition region, the network system will begin to enter the recovery stage.

[0024] As a preference, the two-dimensional numerical matrix D in step 2) σ as follows:

[0025] ;

[0026] Considering the uniformity of the interference intensity value in the injected data and the constraint of the limit impulse, when I m When it is a finite value, the two-dimensional numerical matrix D σ The data values ​​in must meet the following conditions at the same time:

[0027] ;

[0028] Among them, I m is the minimum value of the limit impulse of each dimension of the phase vector space, that is, .

[0029] Preferably, the method for calculating the phase point position change in step 5) is as follows:

[0030] Assume that in the spth simulation step, the phase point moves to the position coordinate At the next simulation step sp+1, the position of the phase point is , then the change in the phase point position in the sp-th simulation step is for

[0031] ;

[0032] The termination condition of this disturbance impulse injection experiment can be described as:

[0033] .

[0034] Preferably, the first row of the matrix in step 6) is the increasing step value, and the second to (n+1) rows are the components of the position of the phase point in the phase space corresponding to the step moment, represented by the symbol q, that is:

[0035] .

[0036] As a preference, the cell array D in step 7 is eo as follows:

[0037] .

[0038] Preferably, the segmentation process in step 8) is as follows:

[0039] Assume that the initial stable domain of the network system The calibration result is , the components of its center vector are expressed as , the path length is expressed as ;

[0040] For numerical matrices The starting point of the phase point trajectory transition zone is the mrk1th m List, ; The end point is the matrix mrk2 m List, ; These two columns of data correspond to the first and last distances from the initial center position coordinates. The coordinates of the farthest phase point; the farthest distance is d maxm , then

[0041] ;

[0042] mrk1 m and mrk2 m Get the number of columns that meet the following conditions

[0043]

[0044]

[0045]

[0046] .

[0047] Furthermore, the present invention also provides a simulation system, which is used to implement the method, comprising:

[0048] a) an injection times setting unit, configured to receive and store a positive integer cnt to determine the number of interference injection rounds;

[0049] b) Interference parameter generation unit, used to construct a two-dimensional numerical matrix D σ , the cnt interference strength σ m Write the first line, corresponding to the action step sp m Write the second line;

[0050] c) Model initialization unit, used to reset the UAV network system model to a unified initial state and drive it to run until the phase point enters the stable domain;

[0051] d) Perturbation injection unit, used to read the matrix D column by column σ , in each round of simulation, the model is injected with a strength of σ m , continuous step length is sp m impulse interference;

[0052] e) Trajectory monitoring unit, used to calculate the position difference between adjacent simulation steps in real time, and to calculate the position difference between adjacent simulation steps in successive sp end Less than the termination threshold ε within the step end When , a stop signal is sent to the model initialization unit;

[0053] f) Trajectory storage unit, used to save the trajectory of this round as (n+1)×spr after receiving the stop signal m Matrix D trcm , and organized into a cell array D by round eo ;

[0054] g) Segment analysis unit, used for group D eo Each D in trcm Transition region identification and data segmentation are performed to output the deviation-recovery characteristics of the phase point trajectory.

[0055] Preferably, the interference parameter generation unit is provided with an intensity uniformity algorithm for uniformly distributing the intensity of each σ under a given limit impulse constraint. m .

[0056] Preferably, the termination threshold ε of the trajectory monitoring unit end and the number of consecutive steps sp end It is a user-adjustable parameter and can be dynamically updated during system operation.

[0057] Furthermore, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which enables a computer to implement the method when the program is executed by a processor.

[0058] Furthermore, the present invention also provides a computer program product, comprising a computer program or instructions, which implement the method when executed by a processor.

[0059] Due to the adoption of the above technical solution, the present invention can significantly improve the scientificity, efficiency and data availability of the test compared with the existing disturbance assessment methods for UAV networks. The specific technical effects are reflected in the following five aspects:

[0060] 1. Two-dimensional limit constraints ensure safe and complete test boundaries: Through the two-dimensional matrix Dσ of interference intensity-action step size, each impulse is explicitly limited to a tolerable range. This avoids the two types of waste, "excessive perturbation premature divergence" and "ineffective weak perturbation" that occur in continuous scanning or Monte Carlo randomization strategies, and improves the effective sample rate of the experiment.

[0061] 2. A unified stability domain starting point ensures repeatable comparison results: The model initialization unit restores the system to the same stability domain center before each injection round to eliminate response deviations caused by initial state differences. This ensures that the trajectory offsets and recovery times under different impulse combinations are strictly comparable, facilitating subsequent control parameter calibration and robustness optimization.

[0062] 3. Termination threshold-continuous step size criterion quickly captures the recovery moment: The trajectory monitoring unit uses the dual conditions of "displacement threshold ε_end + continuous step size sp_end" to determine whether the system has returned to a steady state, without the need for manual observation or subjective trimming. While ensuring judgment accuracy, it significantly shortens invalid simulation steps, saving an average of 30%–50% of computing time.

[0063] 4. Standardized trajectory matrix and automatic segmentation for efficient batch analysis: trajectory matrix D trcm Using a unified format of "step sequence + coordinate sequence", the segmented analysis unit automatically marks the transition zone based on the maximum deviation search algorithm. When batch processing 1000 injection experiments, the "maximum offset distance-recovery time" scatter point set and statistical distribution can be obtained in one step without manual screening.

[0064] In summary, the present invention can not only quantitatively characterize the maximum deviation and recovery time of UAV networks under instantaneous impulse interference, but also greatly reduce the evaluation cost through a unified experimental process and automated data processing, providing reliable, efficient and reusable basic data and decision-making basis for swarm UAV fault-tolerant control, mission safety margin design and abnormal diagnosis algorithm training. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 Basic flow chart of the disturbance impulse injection simulation method.

[0066] Figure 2 Perform formation flying missions for drone network systems.

[0067] Figure 3 There are two stages of the UAV formation flight mission.

[0068] Figure 4 is the phase space of different task stages.

[0069] Figure 5 A diagram showing wind strength.

[0070] Figure 6 These are the five phase point trajectories of the perturbation impulse injection experiment in two stages. DETAILED DESCRIPTION

[0071] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0072] The disturbance impulse injection experiment of the present invention refers to a simulation experiment for obtaining the phase point trajectory characteristics of the system by injecting a certain disturbance within the limit impulse range into the network system model. Figure 1 The basic process of disturbance impulse injection experiment for specific interference is given. The specific steps of disturbance impulse injection simulation experiment are as follows:

[0073] Step 1. Set the number of interference injections cnt

[0074] According to the experimental time cost and simulation computing resources, set as many injection times as possible within an appropriate range.

[0075] Step 2. Determine the value of the injected interference intensity data

[0076] The injected interference intensity data format is a 2×cnt numerical matrix D σ The first line of the matrix is ​​the interference intensity, and the second line is the action step (time), that is,

[0077] (1.1)

[0078] Considering the uniformity of the interference intensity value in the injected data and the constraint of the limit impulse, when I mWhen is a finite value, the data values ​​in formula (1.1) must simultaneously meet the following conditions:

[0079] (1.2)

[0080] Among them, I m It is the minimum value of the limit impulse of each dimension of the phase vector space, that is, I m =min{I lim 1 ,...,I lim n}.

[0081] Step 3. Prepare the network system model

[0082] Fix the initial state of the network system model and start the model to run to a stable state. Ensure that the network system model remains in the same stable state each time interference is injected.

[0083] Step 4. Inject the value of column i of the interference intensity data matrix

[0084] In the disturbance impulse injection module of the model, set the disturbance intensity to σ m , the action duration is sp m , so that the simulation model continues to run.

[0085] Step 5. Record and observe phase point trajectory data

[0086] Monitor the changes of the phase point in the phase vector space in real time in the phase point trajectory observation window. When the change of the phase point position is observed in the continuous sp end less than the termination threshold ε within the step size end When the network operation is terminated, the process goes to step 6; otherwise, the process continues to go to step 5. end =10, sp end =10 -5 .

[0087] The calculation method of the phase point position change is as follows:

[0088] Assume that in the spth simulation step, the phase point moves to the position coordinate At the next simulation step sp+1, the position of the phase point is , then the change in the phase point position in the sp-th simulation step is for:

[0089] (1.3)

[0090] The termination condition of this disturbance impulse injection experiment can be described as:

[0091] (1.4)

[0092] Step 6. Save the phase point trajectory data of the mth injection experiment

[0093] The phase point trajectory data of the i-th perturbation impulse injection experiment is saved as (n+1)×spr m The numerical matrix D trcm , where spr m is the number of simulation steps from the time of interference injection to the time of complete recovery to the stable operation state. The first row of the matrix is ​​the increasing step value, and the second to (n+1) rows are the components of the position of the phase point in the phase space corresponding to the step moment, represented by the symbol q, that is,

[0094] (1.5)

[0095] Step 7. Obtain disturbance impulse injection experimental data

[0096] Inject cnt disturbance impulses into all numerical matrices D obtained from the experiment trcm (m=1,...,cnt) is saved as a cell array D eo , so that the data can be processed later, that is

[0097] (1.6)

[0098] Step 8. Segmentation processing method of experimental data

[0099] The experimental data of the disturbance impulse injection D eo The purpose of segmentation is to find the transition region of the phase point trajectory corresponding to each numerical matrix in the cell array. After the phase point passes through the transition region, the network system will begin to enter the recovery phase. The determination of the phase point trajectory transition region is achieved by marking the starting point and the end point of the region. After completing the marking of these two points, the numerical matrix D trcm The data will be divided into three segments, so this data processing process is called segmentation processing of experimental data. The segmentation processing method is described in detail below.

[0100] Assume that the initial stable domain of the network system The calibration result is , the components of its center vector are expressed as , the path length is expressed as For numerical matrices The starting point of the transition zone of the phase point trajectory is the first The end point is the first column of the matrix Columns, these two columns of data correspond to the first and last distances from the initial center position coordinates The coordinates of the farthest phase point. Let the farthest distance be d max m, then there is

[0101] (1.7)

[0102] mrk1 m and mrk2 m Select the number of columns that meet the following conditions:

[0103] (1.8),

[0104] (1.9),

[0105] (1.10),

[0106] (1.11).

[0107] The case study object of the present invention is a multi-UAV formation network system that performs formation flight tasks at a fixed cruising altitude as Figure 2 shown. The network system consists of 9 homogeneous rotor UAVs, which are required to form a "square" formation and maintain the formation flying along the planned trajectory.

[0108] The multi-UAV network system adopts a "leader-follower" formation strategy. The No. 1 UAV in the leading position is the leader of the entire network system, and the No. 2-5 UAVs are the first-level followers, and the No. 6-9 UAVs are the second-level followers. At the beginning of the mission, 9 homogeneous rotor UAVs take off vertically from the ground synchronously to the cruising altitude, and then form the required formation by moving in the xoy plane at this altitude. Then, the No. 1 UAV receives the path planning result and flies along the planned path according to the path tracking strategy. At the same time, the No. 2-5 UAVs obtain the real-time position information of the No. 1 UAV and maintain the corresponding relative distance; while the No. 6-9 UAVs obtain the position information of the adjacent first-level followers at the same time and maintain the corresponding relative distance. Under the above strategy, the multi-UAV network system realizes integral formation flight.

[0109] The flight mission of the UAV formation can be divided into two stages, namely the formation stage and the formation flight stage.

[0110] Stage 1: Formation stage

[0111] Given the initial position coordinates of each UAV in the takeoff area. In the formation stage, the UAVs will fly from their respective initial positions to the formation positions, so that the entire cluster forms a stable "square" formation in the formation area.

[0112] The UAVs that arrive at the formation position in advance will hover and wait until all 9 UAVs reach the designated formation positions, and the entire cluster will execute the flight mission of the second stage.

[0113] Phase II: Formation flight phase

[0114] After the swarm completes formation, UAV No. 1 will fly along the planned path, and other UAVs will fly according to the following rules. The entire swarm will complete the task of flying along the planned path in a "cross" formation.

[0115] The two phases of the task are as Figure 3 shown. The main task requirement of this UAV formation network is to fly as a whole in a "cross" formation along the planned path. In order to describe the task completion effect, two questions need to be answered during the overall flight of the network: (1) Whether the formation of the UAV network can always maintain the specified shape; (2) Whether the UAV network can always fly on the planned path. The first question focuses on the formation state of the UAV formation network, and the second question focuses on the position state of the UAV formation network. For the formation state and position state, this case respectively proposes to use the formation deformation amount and position offset amount for quantitative measurement. According to the above analysis idea, the corresponding state analysis table of this case is shown in Table 1.

[0116] Table 1 State analysis table of UAV formation network

[0117]

[0118] In the first stage of the task, the UAV formation network needs to complete the formation task. At this time, only the formation state of the network needs to be concerned; while in the second stage of the task, it is required that the UAV formation network not only maintains the formation but also flies along the planned path. At this time, both the formation state and position state of the network need to be concerned. Therefore, in the formation stage, the phase space of this UAV formation network is a 1D space spanned by vector elements representing the formation state. In the formation flight stage, the phase space is a 2D space spanned by vector elements representing the formation state and vector elements representing the position state, as Figure 4 .

[0119] This invention analyzes the types of interference that the case UAV network has to face from two aspects: the self-fault of the UAV formation network and external environmental factors, and gives a quantitative expression of the interference intensity.

[0120] (1) Identify the types of interference

[0121] Based on the state analysis results, the formation and position status of the drone formation network are of particular concern. According to the drone formation network operation rules listed in Table 2, normal communication between drones is essential for maintaining the stability of the network system's formation and position. If a drone experiences a communication failure, preventing it from receiving and sending information, the formation and position status of the entire drone formation will inevitably be affected. Therefore, communication failures of a single drone are the first type of interference that the drone formation in this case must withstand.

[0122] In addition, the UAV formation network is subject to the influence of natural wind during flight, which may cause changes in the formation state and position state. Therefore, the wind force generated by natural wind is the second type of interference experienced by the UAV formation in this case.

[0123] (2) Definition of interference intensity

[0124] Interference 1: Communication failure of a single drone

[0125] In this example, all nine drones in a drone formation network may experience communication failures. However, due to the interconnectedness of the formation structure, communication failures affecting different drones will have varying degrees of impact on the network system's formation and positional states. For example, during formation flight, if drone #1 experiences a communication failure, drones #2-5 will be unable to fly normally. Drones #6-9, following drones #2-5, will also be unable to determine the correct flight direction, causing the entire network to instantly lose its correct position update direction. On the other hand, if drone #6 experiences a communication failure, only drone #6 will be unable to determine its next flight direction, while the other drones will remain unaffected. In reality, the intensity of interference caused by drone communication failures is related to the number of connected drones.

[0126] In complex network theory, the degree of a node refers to the number of edges connected to it, which is related to the intensity of communication failures. This case uses the concept of node degree to quantitatively describe the intensity of communication failures.

[0127] The degree of drone i is deg(i). The number of drones with communication failure at a certain moment is recorded in set A. The intensity of the interference caused by the communication failure at that moment is:

[0128] (1.12);

[0129] From formula (1.12), we can see that the communication failure intensity is a finite number of values ​​in the interval [0,1]. For example, when UAV 1 and UAV 9 have communication failures at the same time, the communication failure intensity of the UAV formation network is:

[0130] (1.13);

[0131] In particular, This indicates that no drones experienced communication failures; Indicates that all drones have communication failures at the same time.

[0132] Interference 2: Wind

[0133] This case uses a simplified Newtonian force to describe the strength of the wind, denoted as σw, and assumes that the wind force acts on nine drones simultaneously.

[0134] σw is a vector, and its component form is denoted as [σw ,x ,σw ,y ], respectively represent the wind strength in the x direction and y direction, such as Figure 5 The magnitude of wind force σw is described by the 2-norm of the vector, i.e. .

[0135] This case study only conducted disturbance impulse injection experiments for wind power in Phase 1 and for a single UAV communication failure in Phase 2. The wind power injection and subsequent analysis in Phase 2 were similar to those in Phase 1.

[0136] The number of interference injections is set to cnt = 5. The five sets of injection data determined according to Equations (1.1) and (1.2) for the wind force in stage 1 and the single UAV communication failure interference in stage 2 are shown in Tables 2 and 3, respectively.

[0137] Table 2 Wind injection data for phase 1

[0138]

[0139] Table 3 Communication fault injection data of a single UAV in phase 2

[0140]

[0141] The obtained phase point trajectories of the disturbance impulse injection experiments in stage 1 and stage 2 are as follows: Figure 6 As shown in (a) and (b).

[0142] According to the algorithms in Equations (1.7) and (1.8)–(1.11), the data matrices from the 10 perturbation impulse injection experiments were segmented, marking the starting point mrk1 and the ending point mrk2 of the phase point trajectory transition region. The results of the segmented data processing are listed in Table 4.

[0143] By observing the data in Table 4, we can preliminarily analyze that the recovery mechanism of the UAV formation network has the following characteristics:

[0144] (1) The transition region of the phase point trajectory of the 10 sets of disturbance impulse injection experimental data is a single point, that is, mrk1=mrk2. This indicates that the network recovery process is smooth and the phase point does not oscillate at the inflection point.

[0145] Table 4. The starting and ending points of the transition zone of the 10 sets of phase point trajectory data of the UAV formation network

[0146]

[0147] (2) The recovery process of the five groups of data in stage 1 is symmetrical with the phase point change process, and their spr m , mrk1, and mrk2 take on almost identical values. This indicates that the total recovery time and recovery point of the drone network have little to do with the magnitude of the impulse generated by the interference. Within the extreme impulse range, the larger the impulse generated by the interference, the faster the recovery.

[0148] (3) The transition region of the phase point trajectory in stage 2 is basically at the end of the entire data set. This indicates that within the extreme impulse range, the network will recover quickly. This is actually related to the parameter settings of the PID controller in the network's path tracking rules and the drone following rules. Different parameters reflect different recovery forces.

[0149] The above is a description of the embodiments of the present invention. The above description of the disclosed embodiments will enable those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined in this invention may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0150] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0151] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0152] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0153] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0154] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0155] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0156] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

Claims

1. A method for simulating disturbance impulse injection in a UAV network system, characterized in that: The method comprises the following steps: 1) Set the number of injections: preset a positive integer cnt, which represents the number of disturbance injection rounds to be performed in the disturbance impulse injection simulation; 2) Generate interference parameter matrix: Construct a two-dimensional numerical matrix D σ , where the first row is the interference intensity of each interference injection, and the second row is the corresponding action step length; 3) Prepare the network system model: fix the initial state of the network system model, start the network system model, and run it to a stable state; ensure that the network system model remains in the same stable state each time interference is injected; 4) Inject the value of the i-th column of the interference parameter matrix: Set the interference intensity to σ in the disturbance impulse injection module of the network system model m , the action step length is sp m , so that the network system model continues to run; 5) Record and observe phase point trajectory data: monitor the changes of phase points in the phase vector space in real time in the phase point trajectory observation window; When it is observed that in the recovery phase of the network system model, the change in the phase position is continuous sp end less than the termination threshold ε within the step size end , terminate the network system model and proceed to step 6); otherwise, continue to execute step 5); 6) Save the phase point trajectory data of the mth disturbance injection: Save the phase point trajectory data of the ith disturbance impulse interference injection as (n+1)×spr m The numerical matrix D trcm , where spr m is the number of simulation steps from the time the disturbance is injected to the time it is fully recovered to the stable operating state; 7) Get the disturbance impulse injection simulation data: inject cnt disturbance impulses into all the numerical matrices D obtained by simulation trcm (m=1,…,cnt) is saved as a cell array D eo ; 8) Segment processing: inject the disturbance impulse into the cell array D eo Segmented processing is performed to find the transition region of the phase point trajectory corresponding to each numerical matrix in the cell array. After the phase point passes through the transition region, the network system model will begin to enter the recovery stage.

2. The method according to claim 1, wherein: Step 2) Two-dimensional numerical matrix D σ as follows: ; Considering the uniformity of the interference intensity value of each injection and the constraint of the limit impulse, when I m When it is a finite value, the two-dimensional numerical matrix D σ The data values ​​in must meet the following conditions at the same time: ； Among them, I m is the minimum value of the limit impulse of each dimension of the phase vector space, that is, 。 3. The method according to claim 2, wherein: The calculation method of the phase point position change in step 5) is as follows: Assume that in the sp-th simulation step, the phase point moves to the position coordinate At the next simulation step sp+1, the position of the phase point is , then the change in the phase point position in the sp-th simulation step is for ； The termination condition of this disturbance impulse injection experiment can be described as: 。 4. The method according to claim 3, wherein: In step 6), the first row of the numerical matrix is ​​the increasing number of simulation steps, and the second to (n+1) rows are the components of the position of the phase point in the phase vector space corresponding to the step time, represented by the symbol q, that is: ； In step 7, the cell array D eo as follows: 。 5. The method according to claim 4, characterized in that: The segmentation method in step 8) is as follows: Assume that the initial stable domain of the network system model is The calibration result is , the components of its center vector are expressed as , the path length is expressed as ; For numerical matrices D trc m ,m=1,..., cnt The starting point of the transition zone of the phase point trajectory is the first List, ; The end point is the first List, ; These two columns of data correspond to the first and last components of the distance center vector respectively The coordinates of the farthest phase point; the farthest distance is , then ； and Get the number of columns that meet the following conditions: ， ， ， 。 6. A simulation system, characterized in that: The system is used to implement the method according to any one of claims 1 to 5, comprising: a) an injection times setting unit, used to preset and store a positive integer cnt to determine the interference injection round; b) Interference parameter generation unit, used to construct a two-dimensional numerical matrix D σ , the cnt interference strength σ m Write the first line, corresponding to the action step sp m Write the second line; c) Model initialization unit, used to reset the UAV network system model to a unified initial state and drive it to run until the phase point enters the stable domain; d) Perturbation injection unit, used to read the matrix D column by column σ , the interference intensity σ of each interference injection m , continuous step length is sp m impulse interference; e) Trajectory monitoring unit, used to calculate the position difference of adjacent simulation steps in real time, and end Less than the termination threshold ε within the step end When , a stop signal is sent to the model initialization unit; f) Track storage unit, used to save the current phase point track data as (n+1)×spr after receiving the stop signal m Matrix D trcm , and organized into a cell array D by round eo ; g) Segment analysis unit for cell array D eo Each D in trcm Transition region identification and data segmentation are performed to output the deviation-recovery characteristics of the phase point trajectory.

7. The system according to claim 6, characterized in that: The interference parameter generation unit is provided with an intensity uniformity algorithm for uniformly distributing the various σ under a given limit impulse constraint. m .

8. The system according to claim 6, characterized in that: The termination threshold ε of the trajectory monitoring unit end and the number of consecutive steps sp end It is a user-adjustable parameter and can be dynamically updated during system operation.

9. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the computer is enabled to implement the method according to any one of claims 1 to 5.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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