Unmanned aerial vehicle network system disturbance impulse injection simulation method and system, medium and program product

Abstract

The invention relates to the technical field of unmanned aerial vehicle cluster elastic control, in particular to an unmanned aerial vehicle network system disturbance impulse injection simulation method and system, a medium and a program product. The method comprises the steps of setting injection rounds, generating an interference parameter matrix, initializing a network model, injecting interference round by round, recording a phase point trajectory, judging recovery based on a termination condition, storing standardized trajectory data, and automatically identifying a transition region in the trajectory. The matching system realizes automation of interference injection, track monitoring, recovery judgment and data segmentation processing, and can output key indexes such as maximum offset and recovery time. The method has the advantages of standard test structure, uniform data format, high adaptability and high efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of elastic control of UAV swarms, and particularly to a simulation method, system, medium, and program product for injecting disturbance impulses into a UAV network system. Background Art

[0002] In recent years, homogeneous network systems composed of multiple autonomous unmanned aerial vehicles (UAVs) have been widely applied in scenarios such as disaster survey, environmental monitoring, material delivery, and formation performance. Different from traditional single-vehicle control, UAV network systems rely on real-time communication and collaborative decision-making among nodes to complete group tasks. Their overall performance depends not only on the maneuverability of individual vehicles but also on the coupled dynamic behavior and robustness at the network level. During actual operation, random factors such as communication interruption, node failure, sudden wind fields, and external electromagnetic interference act on network nodes in the form of "transient impulses", causing the phase points (or state points) to deviate from the established trajectories. If the system lacks a rapid recovery mechanism, the task may fail at best, or even lead to swarm collapse or safety accidents at worst.

[0003] To analyze the risks brought by such disturbances, several evaluation methods have been proposed in the academic and industrial communities. Existing technologies can be roughly divided into three categories:

[0004] 1. Analytical stability evaluation

[0005] Researchers usually abstract UAV networks into coupled differential equations and derive the local stability conditions of the system under small signals by means of Lyapunov theory or spectral radius criterion. Such methods can give closed-form invariant sets or convergence domains, but the premise is to assume that the disturbance amplitude approaches zero infinitely. For impulse disturbances with finite amplitudes and variable action durations, it is difficult for analytical methods to cover the two-dimensional space of intensity - duration simultaneously, nor can they quantify indicators such as "maximum offset" and "recovery time" in the nonlinear recovery process.

[0006] 2. Continuous disturbance scanning simulation

[0007] Another approach is to apply continuous periodic disturbances (such as sine or random noise) in discrete-time simulation, record the system response, and statistically analyze the steady-state error or amplitude amplification factor. Although this method can reflect certain dynamic characteristics, it mainly targets continuous inputs. Sudden impulses often change the system momentum within an extremely short time and then quickly withdraw. Continuous scanning cannot accurately depict the typical process of "instantaneous injection - withdrawal - recovery", nor can it easily obtain a set of comparable data directly corresponding to the impulse amplitude and action step size.

[0008] 3. Monte Carlo random experiment

[0009] Some industrial tests directly perform a large number of Monte Carlo simulations offline: applying random perturbations to random nodes at random times and observing whether the system eventually stabilizes. Although this strategy has a wide coverage, it has three deficiencies:

[0010] 1) Low efficiency: To ensure statistical significance, thousands or even tens of thousands of simulations need to be run, resulting in huge time and computing power consumption;

[0011] 2) Chaotic data organization: Lack of a unified trajectory format and segmentation processing rules, making it difficult to conduct quantitative comparisons;

[0012] 3) Lack of limit constraints: Random values may exceed the range that the system can bear, causing divergence in the early stage of the simulation, wasting resources and making it difficult to extract boundary information. Summary of the Invention

[0013] To solve the above technical problems, the purpose of the present invention is to provide a simulation method for injecting disturbance impulses into a drone network system. By establishing a two-dimensional interference parameter matrix, designing a unified termination criterion, saving a standardized trajectory matrix, and automatically performing transition zone segmentation, this method can not only ensure that each round of injection is based on the same stable domain and ensure that the impulse data meets the limit constraints, but also output the "maximum deviation - recovery time" index that can be directly compared, thereby providing a quantitative basis for the controller design, fault tolerance mechanism optimization, and task safety margin analysis of the drone network.

[0014] To achieve the above purpose, the present invention adopts the following technical solutions: [[ID=1⑨]]

[0015] A simulation method for injecting disturbance impulses into a drone network system, the method comprising the following steps:

[0016] 1) Set the number of injections: Preset a positive integer cnt, representing the number of rounds of disturbance injections to be carried out in this experiment;

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

[0018] 3) Prepare the network system model: Fix the initial state of the network system model and start the model to make it run to a stable state; ensure that the network system model remains in the same stable state every time a disturbance injection is made;

[0019] 4) Inject the i-th column value of the interference intensity data matrix: Set the disturbance intensity to σ m in the disturbance impulse injection module of the model, and the action duration step length to sp m , and make the simulation model continue to run;

[0020] 5) Record and observe the phase point trajectory data: Continuously monitor the changes of the phase point in the phase vector space in the phase point trajectory observation window; when it is observed that during the network system recovery stage, the change amount of the phase point position is less than the termination threshold ε within consecutive sp end step lengths end , terminate the network operation and proceed to step 6); otherwise, continuously execute step 5);

[0021] 6) Save the phase point trajectory data of the m-th injection experiment: Save the phase point trajectory data of the i-th perturbation impulse injection experiment as a numerical matrix D of (n + 1)×spr m , where spr trcm is the number of simulation steps experienced from the interference injection to the complete recovery to the stable operation state; m

[0022] 7) Obtain the perturbation impulse injection experiment data: Save all the numerical matrices D trcm (m = 1, …, cnt) obtained from the cnt perturbation impulse injection experiments as a cell array D eo ;

[0023] 8) Segment processing: Perform segment processing on the perturbation impulse injection experiment data D eo to find the transition region of the phase point trajectory corresponding to each numerical matrix in this cell array. After the phase point passes through this transition region, the network system will start to enter the recovery stage.

[0024] Preferably, the two-dimensional numerical matrix D σ in step 2) is as follows:

[0025] ;

[0026] Considering the uniformity of the interference intensity values in the injection data and the constraint of the limit impulse, when I m is a finite value, the data values in the two-dimensional numerical matrix D σ must simultaneously satisfy the following conditions:

[0027] ;

[0028] where I m is the minimum value of the limit impulses in each dimension of the phase vector space, that is .

[0029] Preferably, the calculation method of the phase point position change amount in step 5) is as follows:

[0030] Assume that at the sp-th simulation step, the phase point moves to the position coordinate , and 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 central 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 for implementing the method, including:

[0048] a) An injection times setting unit for receiving and saving a positive integer cnt to determine the interference injection rounds;

[0049] b) An interference parameter generation unit for constructing a two-dimensional numerical matrix D σ , writing cnt interference intensities σ m into the first row, and writing the corresponding action step size sp m into the second row;

[0050] c) A model initialization unit for resetting the UAV network system model to a unified initial state and driving it to run until the phase point enters the stable domain;

[0051] d) A perturbation injection unit for reading matrix D column by column σ , and injecting an impulse interference with an intensity of σ m and a continuous step size of sp m into the model in each round of simulation;

[0052] e) A trajectory monitoring unit for calculating the position difference between adjacent simulation steps in real time, and sending a stop signal to the model initialization unit when the difference is less than the termination threshold ε end within consecutive sp end steps;

[0053] f) A trajectory storage unit for saving the trajectory of this round as an (n + 1) × spr m matrix D trcm and organizing it into a cell array D eo by rounds after receiving the stop signal;

[0054] g) A segmented analysis unit for performing transition zone identification and data segmentation on each D eo in the array D trcm to output the deviation-recovery characteristics of the phase point trajectory.

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

[0056] Preferably, the termination threshold ε end of the trajectory monitoring unit and the number of consecutive step sizes sp end are user-adjustable parameters and can be dynamically updated during the operation of the system.

[0057] Furthermore, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the computer implements the method described above.

[0058] Furthermore, the present invention also provides a computer program product, including a computer program or instruction, and when the computer program or instruction is executed by a processor, the method described above is implemented.

[0059] Due to the adoption of the above technical solutions, compared with the existing means for evaluating disturbances in the UAV network, the "method" of the present invention can significantly improve the scientific nature, efficiency, and data availability of the experiment. The specific technical effects are reflected in the following five aspects:

[0060] 1. Double-dimensional limit constraints ensure a safe and complete test boundary: Through the interference intensity–action step two-dimensional matrix Dσ, each impulse is explicitly limited within an acceptable interval; avoiding the two types of wastes of "premature divergence of excessive disturbances" and "ineffective weak disturbances" that occur in continuous scanning or Monte Carlo random strategies, and improving the effective sample rate of the experiment.

[0061] 2. Unifying the starting point of the stable region ensures the repeatability of the comparison results: The model initialization unit restores the system to the center of the same stable region before each round of injection, eliminating the response deviation caused by the initial state difference; making the trajectory offset and recovery time under different impulse combinations strictly comparable, facilitating subsequent calibration of control parameters and robustness optimization.

[0062] 3. Termination threshold - continuous step criterion quickly captures the recovery moment: The trajectory monitoring unit determines that the system has reached a new steady state based on the double conditions of "displacement threshold ε_end + continuous step sp_end", without manual observation or subjective clipping; while ensuring the accuracy of the determination, significantly shortening the ineffective simulation steps, and on average saving 30%–50% of the computing time.

[0063] 4. Standardized trajectory matrix and automatic segmentation enable efficient batch analysis: The trajectory matrix D trcm Adopts the unified format of "step sequence + coordinate sequence", and the segmentation analysis unit automatically marks the transition area according to the maximum deviation search algorithm; when batch processing 1000 rounds of injection experiments, the scatter plot set and statistical distribution of "maximum deviation distance - recovery time" can be obtained in one step without manual screening.

[0064] Generally speaking, the present invention can not only quantitatively describe the maximum deviation and recovery time of the UAV network 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 the fault tolerance control of swarm UAVs, the design of task safety margins, and the training of anomaly diagnosis algorithms. Description of the Drawings

[0065] Figure 1 It is the basic flowchart of the disturbance impulse injection simulation method.

[0066] Figure 2 For the UAV network system to perform the formation flight mission.

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

[0068] Figure 4 They are the phase spaces of different mission stages. [[ID=1"]]

[0069] "" Figure 5 It is the schematic diagram of wind intensity.

[0070] Figure 6 They are the 5-phase point trajectory diagrams of the disturbance impulse injection experiment in two stages. Specific implementation manners [[ID=]]

[0071] Combined with the embodiments of the present invention below, the technical solutions in the embodiments will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

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

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

[0074] According to the time cost of the experiment and the computing resources of the simulation, within a suitable range, set as many injection times as possible.

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

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

[0077] (1.1)

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

[0079] (1.2)

[0080] where I m is the minimum value among the limit impulses of each dimension in 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 until it reaches a stable state. Ensure that the network system model remains in the same stable state every time interference is injected.

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

[0084] Set the interference intensity to σ m in the perturbation impulse injection module of the model, with the action duration step size being sp m , and make the simulation model continue to run.

[0085] Step 5. Record and observe the 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 it is observed that during the network system recovery phase, the change amount of the phase point position is less than the termination threshold ε end within consecutive sp end step sizes, terminate the network operation and enter Step 6; otherwise, continue to execute Step 5. In the present invention, the default values sp end = 10, sp end = 10 -5 are taken.

[0087] Among them, the calculation method of the change amount of the phase point position is as follows:

[0088] Assume that at the sp-th simulation step, the phase point moves to the position coordinate , and at the next simulation step sp + 1, the position of the phase point is , then the change amount of the phase point position at the sp-th simulation step is:

[0089] (1.3)

[0090] The termination condition of this perturbation 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 Take 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 cruise altitude as Figure 2 shown. The network system consists of 9 homogeneous rotor UAVs, which are required to complete the formation of a "field" shape and maintain this formation to fly 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, 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, the 9 homogeneous rotor UAVs take off vertically synchronously from the ground to the cruise altitude, and then form the required formation shape through movement 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 simultaneously obtain the position information of the adjacent first - level followers 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 take - off 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 "field" shape in the formation area.

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

[0113] Phase II: Formation Flight Phase

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

[0115] The two phases of the task are as Figure 3 shown. The most important task requirement of this UAV formation network is to fly as a whole in a "tian zi" formation along the planned path. In order to describe the task completion effect, during the whole flight of the network, two questions need to be answered: (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 the position state, this case respectively proposes to use the formation deformation quantity and the position offset quantity 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 phase of the task, the UAV formation network needs to complete the teaming up task, and at this time only the formation state of the network needs to be concerned; while in the second phase 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, the formation state and the position state of the network need to be concerned simultaneously. Therefore, in the teaming up phase, the phase space of this UAV formation network is a 1D space spanned by the vector elements representing the formation state, and in the formation flight phase, the phase space is a 2D space spanned by the vector elements representing the formation state and the vector elements representing the position state, as Figure 4 .

[0119] The present 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 the external environmental factors, and gives a quantitative expression of the interference intensity.

[0120] (1) Identify the types of interference

[0121] According to the status analysis results, the formation status and position status of the UAV formation network are the network statuses that need to be concerned about. According to the UAV formation network operation rules listed in Table 2, the normal communication between UAVs is the basis for ensuring the stability of the network system's formation and position. Once a communication failure occurs in a certain UAV and it cannot obtain and send information, the formation status and position status of the entire UAV formation will definitely be affected. Therefore, the communication failure of a single UAV is the first type of interference that the UAV formation in this case needs to withstand.

[0122] In addition, when the UAV formation network is flying, it will be affected by natural wind, which may cause changes in the formation status and position status. Therefore, the wind force generated by natural wind is the second type of interference that the UAV formation in this case withstands.

[0123] (2)Define the interference intensity

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

[0125] Any of the 9 UAVs in the UAV formation network of this case may have a communication failure. However, due to the connection relationship of the formation structure, the degree of impact on the formation status and position status of the network system caused by the communication failure of different UAVs is different. For example, during the formation flight stage, if UAV No. 1 has a communication failure, UAVs No. 2 - 5 cannot fly normally, and UAVs No. 6 - 9 follow UAVs No. 2 - 5 and cannot determine the correct flight direction, and the entire network will instantly lose the correct position update direction; while if the UAV with a communication failure is UAV No. 6, only itself cannot find the flight direction for the next moment, and the other UAVs are not affected. In fact, the interference intensity generated by the communication failure of a UAV is related to the number of UAVs connected to it.

[0126] In complex network theory, the degree of a node refers to the number of edges connected to the node, which is related to the characteristics of the communication failure intensity here. In this case, the concept of node degree is used to provide a quantitative description of the communication failure intensity.

[0127] Denote the degree of UAV i as deg(i), and the number of the UAV with a communication failure at a certain moment is recorded in set A. Then the intensity of this interference of communication failure at this moment is:

[0128] (1.12);

[0129] As can be seen from Equation (1.12), the communication failure intensity is a finite number of values within the interval [0, 1]. For example, when UAV No. 1 and UAV No. 9 have communication failures simultaneously, the communication failure intensity suffered by this UAV formation network is:

[0130] (1.13);

[0131] In particular, indicates that no communication failure occurs for the UAVs; indicates that communication failures occur simultaneously for all UAVs.

[0132] Interference 2: Wind force

[0133] In this case, the intensity of the wind force is described using the simplified Newtonian force, denoted as σw, and it is assumed that this wind force acts on 9 UAVs simultaneously.

[0134] σw is a vector, and its component form is denoted as [σw ,x , σw ,y , which represent the intensity of the wind force in the x - direction and y - direction respectively, as shown in Figure 5 . The magnitude of the wind force intensity σw is described using the 2 - norm of the vector, that is .

[0135] In this case, only the perturbation impulse injection experiments for the wind force in Phase 1 and the communication failure of a single UAV in Phase 2 are carried out. The wind force injection and subsequent analysis in Phase 2 are similar to those in Phase 1.

[0136] Set the number of interference injection times as cnt = 5. The 5 groups of injection data determined for the wind force in Phase 1 and the communication failure of a single UAV in Phase 2 according to Equations (1.1) - (1.2) are shown in Table 2 and Table 3 respectively.

[0137] Table 2 Wind force injection data in Phase 1

[0138]

[0139] Table 3 Communication failure injection data of a single UAV in Phase 2

[0140]

[0141] The phase - point trajectories of the perturbation impulse injection experiments obtained in Phase 1 and Phase 2 are shown in (a) and (b) of Figure 6 respectively.

[0142] According to the algorithms of Equations (1.7) and (1.8) - (1.11), the data matrix of 10 groups of perturbation impulse injection experiments is segmented, and the starting point mrk1 and the ending point mrk2 of the phase - point trajectory transition region are marked. The results of the data segmentation are listed in Table 4.

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

[0144] (1)The transition regions of the phase point trajectories of the 10 groups of disturbance impulse injection experiment data are all a single point, that is, mrk1 = mrk2. This indicates that the network recovery process is stable and the phase points will not oscillate at the inflection points.

[0145] Table 4 Starting and ending points of the transition regions of the 10 groups of phase point trajectory data of the UAV formation network

[0146]

[0147] (2)The recovery processes and the phase point change processes of the 5 groups of data in the first stage show symmetry, and their spr m , mrk1 and mrk2 almost take the same values. This indicates that the total recovery time of the UAV network and the recovery point have little relation with the magnitude of the impulse generated by the interference. Within the limit impulse range, the greater the impulse generated by the interference, the faster the recovery speed.

[0148] (3)The transition regions of the phase point trajectories in the second stage are basically at the end position of the entire data set. This indicates that within the limit impulse range, the network will recover rapidly. This is actually related to the parameter settings of the PID controller in the network path tracking rule and the UAV following rule. Different parameters reflect different recovery forces.

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

[0150] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0151] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, as well as the combination of flows and / or blocks in the flowchart and / or block diagram. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 one or more of the blocks for implementing the specified functions.

[0152] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 one or more of the blocks.

[0153] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 one or more of the blocks.

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

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

[0156] A computer-readable medium includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. The 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 memory (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 discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media such as modulated data signals and carrier waves.

Claims

1. A simulation method for disturbing impulse injection in an unmanned aerial vehicle network system, characterized in that The method includes the following steps: 1) Set the number of injection times: Preset a positive integer cnt, which represents the number of rounds of interference injection planned for this experiment; 2) Generate an interference parameter matrix: construct a two-dimensional numerical matrix D σ , where the first row is the interference intensity of each round in sequence, and the second row is the corresponding action step length in sequence; 3) Prepare the network system model: Fix the initial state of the network system model and start the model to run it until it reaches a stable state; ensure that the network system model remains in the same stable state every time interference is injected; 4) Inject the value of the i-th column of the interference intensity data matrix: Set the interference intensity to σ in the perturbation impulse injection module of the model m , and the action duration step is sp m , and make the simulation model continue to run; 5) Record and observe the phase point trajectory data: Real-time monitor the changes of the phase point in the phase vector space in the phase point trajectory observation window; When it is observed that during the network system recovery phase, the change amount of the phase point position is less than the termination threshold ε within consecutive sp end step lengths end , terminate the network operation and proceed to step 6); otherwise, continuously execute step 5); 6) Save the phase point trajectory data of the m-th injection experiment: Save the phase point trajectory data of the i-th perturbation impulse injection experiment as a numerical matrix D of size (n+1)×spr m where spr trcm is the number of simulation steps experienced from the start of interference injection to full recovery to the stable operating state; m ​ 7) Obtain experimental data of perturbation impulse injection: Save all the numerical matrices D trcm (m = 1, …, cnt) obtained from cnt times of perturbation impulse injection experiments as a cell array D eo ; 8) Piecewise processing: Process the experimental data D of the disturbance impulse injection eo to perform piecewise processing 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 this transition region, the network system will start to enter the recovery stage.

2. The method according to claim 1, wherein: The two-dimensional numerical matrix D in step 2) σ is as follows: ； Considering the uniformity of the interference intensity value in the injected data and the constraint of the ultimate impulse, when I m is a finite value, the data values in the two-dimensional numerical matrix D σ must satisfy the following conditions simultaneously: ； Among them, I m is the minimum value of the limit impulses in each dimension of the phase space, that is .

3. The method according to claim 2, characterized in that: The calculation method of the phase point position change amount in step 5) is as follows: Assume that at the sp-th simulation step, the phase point moves to the position coordinates At the next simulation step sp+1, the position of the phase point is Then the change in the position of the phase point at the sp-th simulation step is ； The termination condition of this perturbation impulse injection experiment can be described as: 。 4. The method according to claim 3, characterized in that: The first row of the matrix in step 6) is the increasing step size value, and the 2nd to (n + 1)th rows are the components of the position of the phase point corresponding to this step size moment in the phase space, represented by the symbol q, that is: ； Form cell array D in step 7 eo as follows: 。 5. The method according to claim 4, characterized in that: The method of segmented processing in step 8) is as follows: Assume the calibration result of the initial stable region of the network system is , and the components of its central vector are expressed as , and the radial length is expressed as ; For a numerical matrix , the starting point of the phase point trajectory transition region is the th column of the matrix, ; the ending point is the th column of the matrix, ; the data in these two columns respectively correspond to the phase point position coordinates that are the farthest from the initial center position coordinates ; denote this maximum distance as , then there is ； and Take the number of columns that satisfy the following conditions: ， ， ， 。 6. A simulation system, characterized in that, The system for implementing the method according to any one of claims 1-5 includes: a) An injection times setting unit, configured to receive and save the positive integer cnt to determine the number of rounds of interference injection; b) An interference parameter generation unit for constructing a two-dimensional numerical matrix D σ , writing cnt interference intensities σ m into the first row, and writing the corresponding action step length sp m into the second row; c) A model initialization unit, configured 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) A perturbation injection unit for reading matrix D column by column σ , injecting an impulse disturbance with an intensity of σ m and a continuous step size of sp m into the model in each round of simulation; e) A trajectory monitoring unit, which is used to calculate the position difference between adjacent simulation steps in real time, and when this difference is less than the termination threshold ε within consecutive sp end steps, send a stop signal to the model initialization unit; end when the difference is less than the termination threshold ε within consecutive sp f) A trajectory storage unit, which is configured to save the current round of trajectory as an (n + 1) × spr m matrix D trcm , and organize it into a cell array D by round eo ; g) A segmented analysis unit for performing transition region recognition and data segmentation on each D in array D eo to output the deviation-recovery characteristics of the phase point trajectory. trcm ​ 7. The system according to claim 6, wherein: The interference parameter generation unit is provided with an intensity equalization algorithm for evenly distributing each 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 continuous step number sp end are user-adjustable parameters and can be dynamically updated during system operation.

9. A computer-readable storage medium, on which a computer program is stored, characterized in that when the program is executed by a processor, the computer implements the method according to any one of claims 1-5.

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 1-5 is implemented.

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