Method, system, storage medium and program for determining the limit impulse of UAV network system

Abstract

The present invention relates to the field of flexible control technology for drone swarms, and more particularly to a method, system, storage medium, and program for determining the limiting impulse of a phase-point trajectory model in a drone network system. This method comprises three core steps: interference intensity extraction, two-stage step-size increment measurement, and linearity determination. While preserving the system's nonlinear and coupling characteristics, it rapidly determines the system's maximum tolerable duration under varying interference intensities and ultimately outputs a linear or nonlinear limiting impulse value. This method boasts high efficiency, strong adaptability, and portable results, making it suitable for formation control parameter design and system safety boundary verification.

Description

Technical Field

[0001] The present invention relates to the technical field of flexible control of unmanned aerial vehicle (UAV) clusters, and in particular to a method, system, storage medium and program for determining the limit impulse of a phase point trajectory model of a UAV network system. Background Art

[0002] In recent years, collaborative unmanned aerial vehicle swarms (UASs) have played an increasingly important role in scenarios such as disaster monitoring, emergency communications, urban logistics, and formation performances. Compared to single drones, UAV networks, through information interconnection, task allocation, and collaborative control, can achieve self-organizing and adaptive swarm intelligence in large-scale, highly dynamic environments. However, the complex coupling and nonlinear characteristics of the networked structure also make it extremely difficult to assess the system's robustness to sudden interference and safety margin.

[0003] Traditional limiting impulse analysis primarily relies on energy-momentum conservation or linearized small-disturbance models used in spacecraft attitude control. This fails to account for the highly nonlinear behavior of multi-UAV networks under mission phasing, state interconnection, and heterogeneous fault modes. Furthermore, existing formation robustness tests typically use fixed-amplitude disturbances or continuous steady-state wind fields as experimental conditions, ignoring the transient impacts common in real-world scenarios, such as single-machine communication loss, pulsed airflow, and electronic jamming pulses, on the overall network. This lack of precise measurement of limiting impulses results in overly conservative safety margins or distorted evaluation metrics, which in turn restricts the optimization of formation control parameters and fault tolerance design.

[0004] Currently, UAV network missions are trending towards complex environments characterized by high density, strong coupling, and the coexistence of multiple faults. To accurately set control parameters, safety margins, and fault tolerance during mission planning, a new method is urgently needed that can automatically determine the system's limiting impulse while balancing model nonlinearity and computational efficiency. Summary of the Invention

[0005] In response to the above-mentioned shortcomings, the present invention proposes a limit impulse measurement method for UAV network systems. This method takes into account both model accuracy and simulation efficiency, and can quickly obtain transient tolerance boundaries in complex, nonlinear, and multi-stage UAV networks, filling the gap in the existing technology in limit impulse measurement.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions:

[0007] A method for determining the limit impulse of a phase point trajectory model of an unmanned aerial vehicle network system, the method comprising the following steps:

[0008] 1) Obtain the interference intensity value range identifier flg1 and the interference intensity continuity characteristic identifier flg2, and set the number of measurements c;

[0009] 2) Based on the logical combination of flg1 and flg2, an interference intensity extraction module is used to determine a set of interference intensity data, wherein the interference intensity is sorted from largest to smallest according to the absolute value of the disturbance to the system;

[0010] 3) Following the principles of decreasing intensity and increasing step size, the measurement module is used to simulate each interference intensity and iteratively determine the maximum action step size that keeps the system stable in the phase space state dimension.

[0011] 4) Input all interference intensity data and the corresponding maximum action step length data into the linear characteristic judgment module to determine whether their reciprocal absolute values ​​have a linear relationship;

[0012] 5) If the linear relationship holds, the average value of the product of each interference intensity and the maximum step length is used as the limit impulse output; if not, the product of the minimum interference intensity and the maximum step length is output as the limit impulse value.

[0013] Preferably, in step 2), the interference intensity extraction module first performs an OR logic operation on flg1 and flg2 to obtain a flag variable flg, and performs the following actions according to the value of flg:

[0014] When flg = 1, it means that the interference intensity value is a continuous infinite interval. The module randomly extracts a value in this interval as the intensity value and injects it into the network system model. The simulation is canceled after 1 step. If the state of the system in the corresponding dimension can be restored to the stable domain, the value is retained. The above process is repeated until c valid intensity values ​​are obtained, and the retained values ​​are sorted from large to small according to absolute value to form an interference intensity sequence;

[0015] When flg = 0, it means that the interference intensity is a finite discrete point. The module processing includes the following two cases:

[0016] ① If the total number of discrete values ​​cx ≤ c, then all discrete values ​​are traversed and injected into the system simulation model respectively. The simulation is canceled after 1 step, and a total of cy intensity values ​​that can be recovered from the system state are retained. They are sorted from large to small by absolute value to form an interference intensity sequence;

[0017] ② If cx > c, randomly extract interference intensity values ​​from all discrete points and inject them into the system simulation model one by one. The simulation is canceled after one step. If the system state can be recovered, the value is retained. Repeat until c valid values ​​are obtained or all cx values ​​have been traversed. Then, sort them from large to small according to their absolute values ​​to form an interference intensity sequence.

[0018] Preferably, the process of measuring the interference intensity data by the measuring module in step 3) follows the following two principles:

[0019] Principle 1: During the entire measurement process, the interference intensity is injected into the network system model in the order of absolute value from large to small, and the limit impulse is measured in sequence;

[0020] Principle 2: When measuring a certain interference intensity data, the interference injection step length under the intensity is stepped in order from small to large.

[0021] Preferably, the linear characteristic judgment module in step 4) judges whether the absolute value of the inverse of the interference intensity data and its corresponding maximum step length data have a sufficiently strong linear relationship; if the linear relationship holds, the interference is in the phase vector space s i The limit impulse generated in the dimension is considered to be a constant; that is,

[0022] When the absolute value of the reciprocal of the interference intensity With phase space s i Maximum action time in dimension When there is a linear relationship, ;in, is the proportional coefficient, which is a constant;

[0023] Then there is , that is, the limit impulse corresponding to the external disturbance is equal to the proportional coefficient , is a constant value, the value of the limit impulse ;

[0024] On the contrary, the limit impulse will exist in a range and require further processing.

[0025] Preferably, the input of the linear characteristic judgment module in step 4) is two array data and ,in,

[0026] ,

[0027] ;

[0028] The output is a Boolean variable, denoted as ，c and cy Indicates the number of interference intensity extractions, is the phase space s i Maximum action time in dimension;

[0029] when When , it shows that the linear relationship holds. When , it indicates that the linear relationship does not hold; The condition is: there is a constant , making

[0030] ;

[0031] in, Linear accuracy.

[0032] As a preference, the output of the measuring device in step 5) is the value of the limit impulse , the Boolean variable flg output by the interference intensity extractor and the Boolean variable output by the linear characteristic judger The value of is determined jointly, namely:

[0033]

[0034] in, Indicates the xth interference intensity extracted, c and cy indicate the number of interference intensities extracted, Indicates the maximum step size of the xth interference intensity output by the detector.

[0035] Furthermore, the present invention also provides a UAV network system limit impulse measurement system for implementing the method, comprising:

[0036] a) Interference intensity extraction module, used to obtain interference intensity data according to flg1 and flg2 control logic;

[0037] b) a determination module, for iteratively simulating interference injection and determining the maximum allowable injection step size at each intensity;

[0038] c) a linear characteristic judgment module, used to judge the linear relationship between the interference intensity and the maximum step length data;

[0039] d) Output module, calculates the limit impulse value based on the linear judgment result.

[0040] Preferably, the interference intensity extraction module includes a random sampling submodule and a verification submodule, the latter being used to determine whether the network system state is recoverable under the action of one simulation step; and / or, the measurement module includes an exponential growth submodule and a binary search submodule, which are used to improve the step search efficiency and locate the maximum allowable step size.

[0041] 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.

[0042] 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.

[0043] By adopting the above technical solution, the present invention achieves the following significant technical effects through the collaborative design of interference intensity extraction, two-stage step search, and linear characteristic judgment:

[0044] 1. Quantified Transient Tolerance Boundary: This paper defines the product of interference intensity and duration as "limit impulse," establishing for the first time a unified, quantifiable evaluation metric for the transient interference immunity of multi-UAV networks. The measured limit impulse value can be directly used as a hard constraint for formation robust control gains, safety redundancy design, and task scheduling, enhancing the targetedness and reliability of system design.

[0045] 2. Adaptive Extraction of Both Discrete and Continuous Disturbances: This invention implements a unified sampling strategy for both discrete faults (node ​​loss, link interruption) and continuous disturbances (pulsed wind fields, sudden acceleration changes) based on the logical combination of the disturbance value range identifier (flg1) and the continuous characteristic identifier (flg2). Disturbance intensity data is injected into the system in descending order, ensuring monotonic comparability of the experimental sequence and avoiding the sample redundancy and sequence sensitivity issues associated with traditional random-repeated experiments.

[0046] 3. Efficient Two-Stage Step Size Search Mechanism: Utilizing a two-stage step size increment strategy combining exponential growth lock zone and bisection approximation convergence, this method rapidly locates the critical step size for system instability within O(logN) complexity, compared to simple linear stepping or global Monte Carlo methods. This strategy significantly reduces the number of simulation iterations and computational overhead by over 50%, meeting the requirements for online evaluation or rapid offline calibration of large-scale drone networks.

[0047] 4. Automatic Linearity Identification and Result Refinement: A linear fit residual evaluation mechanism, based on the absolute value of the inverse of the interference intensity and the maximum step size, automatically distinguishes between linear limits (fixed values) and nonlinear limits (intervals). For linear scenarios, the system directly provides a fixed average impulse value; for nonlinear scenarios, it outputs a minimum impulse upper bound, avoiding result distortion caused by artificial empirical thresholds and ensuring greater reliability and applicability of the technical results.

[0048] 5. Improving Model Accuracy and Versatility: Utilizing a phase-point trajectory model to characterize the multidimensional state evolution and coupling relationships of UAV networks, this approach fully preserves communication topology changes, task switching, and nonlinear dynamics, resulting in measurement results that are closer to the actual operational boundaries. This approach exhibits excellent portability across system scale, topology, and mission modes, enabling direct embedding in various simulation platforms and rapid deployment across scenarios and missions.

[0049] 6. Support for subsequent control and safety verification processes: The limit impulses measured by this method serve as boundary conditions for closed-loop robust controller parameter tuning, fault injection test design, and hardware-software redundancy planning. By accurately defining transient safety margins, traditional empirically based conservative factors can be reduced, improving formation control performance by 5% to 15% while ensuring flight safety levels are maintained.

[0050] 7. Reduce test costs and implementation risks: Compared with actual flight tests, simulation measurements avoid the risk of crashes or equipment damage caused by high-amplitude pulse interference. By calibrating the limit impulse once, the number of subsequent physical test rounds can be reduced by more than 30%, significantly saving test resources and time costs.

[0051] In summary, the present invention not only solves the problem that the existing technology cannot quickly and accurately quantify the transient impulse tolerance of multi-UAV networks, but also greatly improves the measurement accuracy and applicability through efficient search and automatic discrimination mechanisms, providing strong technical support for the robust control and safety redundancy design of UAV formations. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is the basic structure diagram of the limit impulse meter.

[0053] Figure 2 Perform formation flying missions for drone network systems.

[0054] Figure 3 There are two stages of the UAV formation flight mission.

[0055] Figure 4 is the phase space of different task stages.

[0056] Figure 5 A diagram showing wind strength.

[0057] Figure 6 This is a graph showing stress intensity and maximum step length data. DETAILED DESCRIPTION

[0058] 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.

[0059] The purpose of the limit impulse measurement of the present invention is to find out the maximum impulse that the network system can withstand caused by a certain interference, and to provide the boundary of interference injection for the subsequent disturbance impulse injection experiment. After considering the differences in interference intensity types and value ranges, as well as studying the linear and nonlinear performance of multiple rounds of measurement data, the present invention proposes a method for limit impulse measurement using simulation experiments. The basic idea of ​​this method is to first extract the intensity value of the interference according to the intensity characteristics, and then conduct simulation experiments according to the intensity decreasing principle and step increasing principle, iteratively obtain the maximum step length data corresponding to each intensity value, and then judge whether there is a strong linear relationship between the stress intensity data and the maximum step length data, and finally determine the final limit impulse value based on the judgment result. In order to improve the efficiency of the simulation experiment, the measurement method is designed as a limit impulse meter, and its structure is as follows. Figure 1 shown.

[0060] In the following content, the input and output of the limit impulse meter and the implementation methods of its three core modules: interference intensity extractor, meter and linear characteristic judger will be explained in detail.

[0061] 1. Input and output of the measuring instrument

[0062] (1) Input of measuring device

[0063] The input of the limit impulse meter includes the number of measurements c and the range of interference intensity. Discrete characteristic identification of interference intensity The specific meanings of each are as follows.

[0064] The number of measurements c is an integer greater than 0. It is the number of rounds of step-by-step measurement tests and the number of groups of input and output data of the measuring device. In practice, the number of measurements should be set by balancing the two conflicting constraints of model accuracy and time cost. The default value of this invention is .

[0065] Interference intensity value range identifier The establishment of is essentially a classification of interference according to the different ranges of intensity values. It is a Boolean variable with a value of 0 or 1.

[0066] Interference intensity continuous characteristic mark The establishment of is essentially a classification of interference based on whether the intensity is a continuous variable. It is also a Boolean variable with a value of 0 or 1.

[0067] Boolean variables and The values ​​and their corresponding meanings are shown in Table 1.

[0068] Table 1 and The value and its corresponding meaning

[0069]

[0070] (2) Output of the measuring device

[0071] The output of the measuring device is the value of the limit impulse Specifically, it is the result of further processing of the interference intensity data and the maximum step size data. The processing method is a Boolean variable output by the interference intensity extractor. and the Boolean variable output by the linear characteristic judge The value of is jointly determined, that is

[0072] (1.1)

[0073] in, Indicates the extracted x-th interference intensity, c and Indicates the number of interference intensity extractions, Indicates the maximum step size of the xth interference intensity output by the detector.

[0074] In general, when When , the output of the limit impulse meter is the average value of the product of the interference intensity data and its corresponding maximum step length data; when , the limit impulse takes the minimum value of the product of the absolute value of the interference intensity in the measurement data and the corresponding maximum measurement step length.

[0075] 2. Core Module of the Meter

[0076] 1. Interference intensity extraction module

[0077] Two indicator variables related to interference intensity and After OR logic operation, the tag variable is obtained According to the value of The interference intensity extraction module will perform different actions depending on the value.

[0078] (1) When When the impulse is applied, a value is randomly selected within the range of the interference intensity and injected into the network system model as the intensity value. The value is then applied for one simulation step and then canceled. The dimension can return to the stable domain, that is, the network system corresponds to If the dimension can be restored, the value is retained; otherwise, the value is discarded and randomly drawn again until the value is Such values ​​are arranged in descending order according to their absolute values ​​and recorded as .

[0079] (2) When When the interference intensity is taken as a finite number of discrete points, the number of values ​​is recorded as .

[0080] ① When When , all discrete values ​​are injected into the network system model as intensity values, and the effect is cancelled after 1 simulation step. The value of the dimension state that can still be restored, let The external stress intensity values ​​as the measurement input data are obtained and arranged in descending order according to the absolute value and recorded as .

[0081] ② When When , a value is randomly selected from the discrete points where the interference intensity can take values, and this value is injected into the network system model as the intensity value, so that it acts for one simulation step and then is canceled. If the dimension can be restored, the value is retained; otherwise, the value is discarded and randomly drawn again until the value is Such a value or draw all values, arrange them in descending order according to their absolute values, and record them as or .

[0082] 2. Measurement module

[0083] In order to improve the efficiency of the measurement device, the measurement process of the interference intensity data follows the following two principles:

[0084] Principle 1 (principle of decreasing intensity): During the entire measurement process, the interference intensity is injected into the network system model in the order of absolute value from large to small, and the limit impulse is measured in turn.

[0085] Principle 2 (step-increasing principle): When measuring a certain interference intensity data, the interference injection step size under the intensity is stepped in order from small to large.

[0086] 3. Linear characteristic judgment module

[0087] The function of the linear characteristic judgment module is to judge whether the absolute value of the inverse of the interference intensity data and its corresponding maximum step length data have a sufficiently strong linear relationship. If the linear relationship is established, the interference is in the phase vector space. The limit impulse generated in the dimension can be considered as a constant; otherwise, the limit impulse will exist in a range and require further processing.

[0088] When the absolute value of the reciprocal of the interference intensity and phase space Maximum action time on the dimension When there is a linear relationship, there is . Among them, is the proportionality coefficient, which is a fixed constant. Then there is , that is, the limit impulse corresponding to this external interference is equal to the proportionality coefficient , which is a fixed value.

[0089] The input of the linear characteristic judgment module is two array data and , among which,

[0090] (1.2),

[0091] (1.3);

[0092] The output is a Boolean variable, denoted as . When , it indicates that the linear relationship holds. When , it indicates that the linear relationship does not hold. The condition for is that there exists a fixed constant

[0093] (1.4);

[0094] Among them, is the linear accuracy, and the present invention adopts the default value .

[0095] The case study object of the present invention is a multi-UAV formation network system performing formation flight tasks at a fixed cruise altitude as shown in Figure 2 . This network system is composed of 9 homogeneous rotary-wing UAVs, and is required to complete the formation of a "field" formation and maintain this formation to fly along the planned trajectory. [[ID= 56]]

[0096] This 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, 9 homogeneous rotary-wing UAVs take off vertically from the ground synchronously to the cruise 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 simultaneously obtain the position information of the adjacent first-level followers and maintain the corresponding relative distance. Under the above strategy, this multi-UAV network system realizes integral formation flight.

[0097] The flight mission of the UAV formation can be divided into two stages, namely the formation stage and the formation flight stage.

[0098] Stage 1: Formation stage

[0099] The initial position coordinates of each UAV in the take-off area are given. 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.

[0100] The UAVs that arrive at the formation position in advance will hover and wait. It is not until all 9 UAVs reach the designated formation position that the entire cluster will execute the flight mission of the second stage.

[0101] Stage 2: Formation flight stage

[0102] When the cluster completes the formation, UAV No. 1 will fly according to the planned path, and other UAVs will fly according to the following rules. The entire cluster will complete the mission of flying along the planned path in the "square" formation.

[0103] The two stages of the mission are as Figure 3 shown. The most important mission requirement of this UAV formation network is to fly as a whole in the "square" formation along the planned path. In order to describe the mission 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 form; (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 amount and the position offset amount for quantitative measurement. According to the above analysis idea, the corresponding state analysis table of this case is shown in Table 2.

[0104] Table 2 State analysis table of UAV formation network

[0105]

[0106] In the first stage of the mission, the UAV formation network needs to complete the formation mission. At this time, only the formation state of the network needs to be concerned; while in the second stage of the mission, it is required that the UAV formation network not only maintains the formation but also flies according to the planned path. At this time, both the formation state and the position state of the network need to be concerned. Therefore, in the formation stage, the phase space of this UAV formation network is a 1-dimensional space spanned by the vector elements representing the formation state. In the formation flight stage, the phase space is a 2-dimensional space spanned by the vector elements representing the formation state and the vector elements representing the position state, as Figure 4 .

[0107] This paper analyzes the types of interference faced by the case drone network from two aspects: the drone formation network's own faults and external environmental factors, and gives a quantitative expression of the interference intensity.

[0108] (1) Identify the type of interference

[0109] 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.

[0110] 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.

[0111] (2) Definition of interference intensity

[0112] Interference 1: Communication failure of a single drone

[0113] 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.

[0114] 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.

[0115] Note on drones The degree is , the number of the drone that has a communication failure at a certain moment is recorded in the collection , then the interference intensity of the communication failure at this moment is:

[0116] (1.5);

[0117] From formula (1.5), we can see that the communication failure intensity is an interval For example, when UAV No. 1 and UAV No. 9 have communication failures at the same time, the communication failure intensity of the UAV formation network is:

[0118] (1.6);

[0119] In particular, This indicates that no drones experienced communication failures; Indicates that all drones have communication failures at the same time.

[0120] Interference 2: Wind

[0121] This case uses simplified Newtonian force to describe the strength of wind force, denoted as , and assume that the wind force acts on 9 UAVs at the same time.

[0122] is a vector whose components are expressed as , respectively, in Direction and Wind strength in direction, e.g. Figure 5 The magnitude of wind strength Described by the 2-norm of the vector, that is, .

[0123] The following is a detailed description of the determination of the limit impulse of communication failure interference of a single UAV based on the above method.

[0124] (1) Analysis of interference intensity range identification

[0125] According to the interference analysis results, the communication failure intensity of a single UAV The value range is Therefore, the interference intensity range is marked .

[0126] The communication status of each drone has two values: "fault" and "normal". Therefore, there are a total of 9 drone communication failure modes. According to formula (1.5), the 512 modes can generate 23 different discrete stress intensity values, which are arranged in order from small to large in Table 3. Therefore, the continuous characteristic mark of external stress intensity .

[0127] (2) Interference intensity extraction

[0128] In this case, the number of measurements is set First, label the variables , Secondly, although the number of values , but the number of communication failure modes Therefore, corresponding to the situation (2)② in step 2, 512 communication failure modes corresponding to the 23 intensity values ​​in Table 3 should be randomly selected and put into the model for a 1-step injection test. If the simulation model can recover, the value is retained until 24 intensity values ​​are obtained.

[0129] This case presents a special case. Because each interference intensity value corresponds to multiple communication failure modes, after randomly extracting a certain interference intensity value, it is necessary to extract the communication failure mode at that intensity again. For example, there are four communication failure modes corresponding to an intensity of 0.0833, as shown in Table 4. If the intensity value extracted in the first level of random extraction is 0.0833, a second level of random extraction is required to determine the injection mode for this interference intensity.

[0130] Table 3 23 values ​​of communication fault intensity of a single UAV and the corresponding number of failure modes

[0131]

[0132] Table 4 Four communication failure modes with stress intensity of 0.0833

[0133]

[0134] After extraction and one-step injection detection, 24 groups of communication failure modes and interference strengths are determined as the input of the next step detector, see Table 5.

[0135] (3) Limit impulse measurement results

[0136] According to the method of the present invention, the limit impulse of the 24 groups of communication fault interference in Table 5 was measured. During the measurement process, it was found that the communication fault interference of a single drone only affects the formation deformation variable and has no effect on the position offset. Therefore, the communication fault interference of a single drone is in the phase vector space. The limiting impulse in dimension (the dimension representing the position offset) is , in the phase space The measurement results of dimensions (dimensions that characterize the deformation of the formation) are listed in Table 5.

[0137] Table 5 Measurement results of 24 groups of communication fault stress and their corresponding limit impulses

[0138]

[0139] (4) Linear characteristics analysis

[0140] The relationship between the absolute value of the reciprocal of the interference intensity and the maximum step length of the 24 groups in Table 5 is plotted as follows: Figure 6 As shown in the scatter plot, we can see that the 24 groups of data show obvious clustering characteristics, and have neither linear characteristics nor functional characteristics.

[0141] It should be further explained that two pairs of data in Table 5 are special: ① Data in Groups 18 and 19 show that due to differences in communication failure modes, different limit impulses may be measured under the same interference intensity; ② Data in Groups 22 and 23 show that there is not a completely monotonic relationship between interference intensity and maximum action step length. In other words, when the interference intensity is relatively large, the maximum action step length may also be large.

[0142] The reasons for the above two phenomena are analyzed as follows:

[0143] The definition of interference intensity due to a single UAV communication failure is essentially the degree to which a single failure affects the connectivity performance of the swarm network. There is no direct functional relationship between the connectivity of a formation network and the deformation of the formation. Due to the dynamic nature of UAV formation networks, the impact of individual UAV loss of connection on the formation may be inadvertently reduced or increased during the network's movement. For example, although the communication failure pattern corresponding to data set 23 resulted in only UAV 2 losing contact with the swarm, UAVs 6 and 7 required connection to UAV 2 to accurately follow, effectively impacting three UAVs. In contrast, the communication failure pattern corresponding to data set 22 involved UAVs 6 and 8, impacting only two UAVs. Furthermore, UAV 8 was at the front of the formation. After losing contact with the rest of the network, it continued flying at its original speed, inadvertently aligning itself with the overall formation's flight in the x-direction and mitigating the impact of the loss of connection on the formation.

[0144] (5) Calculation of measurement results

[0145] Since the data does not have linear characteristics, according to the third fraction of formula (1.1), the communication failure interference of a single UAV is in the phase vector space The limiting impulse in dimension (the dimension that characterizes the formation deformation) is the minimum value of all the data in the last column of Table 5, that is,

[0146] Phase 1: I CFlim 1 = 89.0477 (1.7),

[0147] Based on the above analysis and measurement process, the limit impulse measurement results of a single UAV communication fault interference in stage 2 are as follows:

[0148] Phase 2: I 2CFlim =[I CFlim 1 ,I CFlim 2 ]=[89.0477,∞](1.8);

[0149] Among them, I CFlim 1 is the limit impulse of communication fault interference stress in the s1 dimension of the phase vector space, I CFlim 2 is the limit impulse of the communication fault interference stress in the s2 dimension of the phase vector space, I 2 CFlim is the limit impulse vector of communication fault interference stress in the phase vector space.

[0150] (6) Analysis of the limit impulse of natural wind interference

[0151] according to Figure 5 Given the given wind model, the nine drones in the drone formation network will experience the same acceleration under the influence of natural wind. Since the nine drones are isomorphic, the network as a whole will deviate from its original position at the same speed. Therefore, it can be inferred that while wind continues to act, the network's formation deformation will remain largely unchanged. When the wind is removed, the drones' teaming and path-tracking rules will cause each drone to move toward the predetermined target location. At this point, the swarm's formation will change due to the differences in movement direction, speed, and acceleration of each drone. However, these changes will gradually recover as the drone formation returns to its target location.

[0152] Under the continuous influence of wind, the drone formation network will continue to deviate from the target position. However, according to the teaming rules and path tracking rules, no matter how far the formation deviates from the target position, once the wind stops, the network system will return to the target position under the control of the motion rules. In other words, as long as the impulse of natural wind interference is a finite value, the network's position offset will always return to a minimum.

[0153] The above analysis shows that the natural wind interference has minimal impact on the phase point trajectory in stage 1, and the changes in the phase point trajectory caused by the natural wind with limited impulse will always recover as the interference stops. Therefore, the limit impulse of the natural wind on the network in both stages is infinite, which can be expressed as:

[0154] Phase 1: I Wlim =∞;

[0155] Phase II: I 2 Wlim =[I Wlim 1 ,I Wlim 2 ]=[∞,∞](1.9);

[0156] Among them, I Wlim is the limit impulse of natural wind stress in stage 1, I2 Wlim is the ultimate impulse vector of natural wind stress in stage 2, I Wlim 1 is the limit impulse of natural wind stress in the s1 dimension of the phase vector space, I Wlim 2 is the limit impulse of natural wind stress in the s2 dimension of the phase vector space.

[0157] There are three main reasons why infinite limit impulse appears here:

[0158] (1) The wind model assumes that the external force exerted by the wind on each drone is uniform;

[0159] (2) 9 drones are considered to be of ideal equal quality;

[0160] (3) There are no constraints such as spatial boundaries and the maximum deviation distance of the drone, and it is assumed that the drone can always obtain the target location information.

[0161] Under these three strong assumptions, the drones in the case can always maintain their formation and return to the target position, and natural wind interference will not cause the network state of the drone formation to diverge.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0168] 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.

[0169] 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 determining the limit impulse of a drone network system, characterized in that: The method comprises the following steps: 1) Obtain the interference intensity value range identifier flg1 and the interference intensity continuous characteristic identifier flg2, and set the number of measurements c; 2) Based on the logical combination of flg1 and flg2, an interference intensity extraction module is used to determine a set of interference intensity data, wherein the interference intensity is sorted from largest to smallest according to the absolute value of the disturbance to the system; 3) Following the principle of decreasing intensity and increasing step size, the measurement module is used to simulate each interference intensity and iteratively determine the maximum action step size that keeps the system stable in the phase space state dimension; 4) All interference intensity data and the corresponding maximum action step length data are input into the linear characteristic judgment module to determine whether the reciprocal absolute values ​​have a linear relationship; 5) If the linear relationship holds, the average value of the product of each interference intensity and the maximum step length is used as the limit impulse output; if not, the minimum interference intensity and the maximum step length product is output as the limit impulse value; In step 2), the interference intensity extraction module first performs an OR logic operation on flg1 and flg2 to obtain a flag variable flg, and then performs the following actions based on the value of flg: When flg=1, it means that the interference intensity value is a continuous infinite interval. The module randomly extracts a value in this interval as the intensity value and injects it into the network system model. The simulation is canceled after one step. If the state of the system in the corresponding dimension can be restored to the stable domain, the value is retained. The above process is repeated until c valid intensity values ​​are obtained, and the retained values ​​are sorted from large to small according to the absolute value to form an interference intensity sequence. When flg=0, it means that the interference intensity is a finite discrete point, and the module processing includes the following two cases: ① If the total number of discrete values ​​cx≤c, then all discrete values ​​are traversed and injected into the system simulation model respectively. The simulation is canceled after one step, and a total of cy intensity values ​​that can be recovered from the system state are retained. They are sorted from large to small according to the absolute value to form an interference intensity sequence; ② If cx>c, then randomly extract interference intensity values ​​from all discrete points and inject them into the system simulation model one by one. The simulation is canceled after one step. If the system state can be recovered, the value is retained. Repeat until c valid values ​​are obtained or all cx values ​​have been traversed, and then sort them from large to small according to absolute value to form an interference intensity sequence.

2. The method according to claim 1, characterized in that The process of measuring the interference intensity data by the measuring module in step 3) follows the following two principles: Principle 1: During the entire measurement process, the interference intensity is injected into the network system model in the order of absolute value from large to small, and the limit impulse is measured in sequence; Principle 2: When measuring a certain interference intensity data, the interference injection step length under the intensity is carried out in order from small to large.

3. The method according to claim 1, characterized in that The linear characteristic judgment module in step 4) judges whether the absolute value of the inverse of the interference intensity data and its corresponding maximum step length data have a sufficiently strong linear relationship; if the linear relationship holds, the interference is in the phase vector space s i The limit impulse generated in the dimension is considered to be a constant; that is, when the absolute value of the inverse of the interference intensity σ With phase space s i Maximum action time in dimension When there is a linear relationship, ; in, k i is the proportional coefficient, which is a constant; Then there is , that is, the limit impulse corresponding to the interference is equal to the proportional coefficient k i , is a constant value, the value of the limit impulse ; On the contrary, the limit impulse will exist in a range and require further processing.

4. The method according to claim 3, characterized in that The input of the linear characteristic judgment module in step 4) is two array data X 1 and X 2 ,in, ， ； The output is a Boolean variable, denoted as , c and cy Indicates the number of interference intensity extractions, is the phase space s i Maximum action time in dimension; when When , it shows that the linear relationship holds. When , it indicates that the linear relationship does not hold; The condition is that there exists a constant k such that ， ； Where ε is the linear accuracy, Indicates the maximum step size of the xth interference intensity output by the detector.

5. The method according to claim 4, characterized in that The output of the measuring device in step 5) is the value of the limit impulse , the flag variable flg output by the interference intensity extractor and the Boolean variable output by the linear characteristic judger The value of is determined jointly, namely: ， in, Indicates the extracted x-th interference intensity.

6. A UAV network system limit impulse measurement system, characterized in that: Used to implement the method according to any one of claims 1 to 5, comprising: a) Interference intensity extraction module, used to obtain interference intensity data according to flg1 and flg2 control logic; b) a determination module, for iteratively simulating interference injection and determining the maximum allowable injection step size at each intensity; c) a linear characteristic judgment module, used to judge the linear relationship between the interference intensity and the maximum step length data; d) Output module, calculates the limit impulse value based on the linear judgment result.

7. The system according to claim 6, characterized in that The interference intensity extraction module includes a random sampling submodule and a verification submodule, the latter of which is used to determine whether the network system state is recoverable under the action of one simulation step; and / or, the measurement module includes an exponential growth submodule and a binary search submodule, which are used to improve the step search efficiency and locate the maximum allowable step.

8. 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.

9. 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.

Images