Method and system for measuring limit impulse of unmanned aerial vehicle network system, storage medium and program

Abstract

The invention relates to the technical field of unmanned aerial vehicle cluster elastic control, in particular to an unmanned aerial vehicle network system phase point trajectory model limit impulse measuring method and system, a storage medium and a program. The method comprises three core steps of interference intensity extraction, double-stage step length increasing measurement and linear characteristic judgment, can quickly determine the maximum tolerable action time of the system under different interference intensities on the premise of keeping the non-linear and coupling characteristics of the system, and finally outputs a linear or non-linear ultimate impulse value. The method has the advantages of high efficiency, high adaptability, result portability and the like, and is suitable for formation control parameter design and system security boundary verification.

Description

Technical Field

[0001] The present invention relates to the technical field of elastic control of unmanned aerial vehicle swarms, and particularly to a method, a system, a storage medium, and a program for determining the ultimate impulse of a phase point trajectory model of an unmanned aerial vehicle network system. Background Art

[0002] In recent years, multi-unmanned aerial vehicle cooperative systems (Unmanned Aerial Vehicle Swarm, hereinafter referred to as "unmanned aerial vehicle network systems") have played an increasingly important role in scenarios such as disaster monitoring, emergency communication, urban logistics, and formation performance. Compared with a single unmanned aerial vehicle, an unmanned aerial vehicle network system can achieve self-organized and adaptive swarm intelligence in a large-scale and high-dynamic environment through information interconnection, task allocation, and cooperative control. However, the complex coupling and non-linear characteristics brought about by the networked structure also make it extremely difficult to evaluate the robustness and safety margin of the system against sudden disturbances.

[0003] Traditional ultimate impulse analysis mainly follows the energy-momentum conservation or linearized small perturbation model in spacecraft attitude control, and it is difficult to cover the highly non-linear behaviors of multi-unmanned aerial vehicle networks in task phasing, state interconnection coupling, and heterogeneous fault modes. In addition, existing formation robustness tests usually use fixed-amplitude perturbations or continuous steady-state wind fields as experimental conditions, ignoring the transient impacts on the overall network caused by common short-term sudden disturbances (such as single-machine communication loss, pulsed airflow, and electronic interference pulses) in actual scenarios. The lack of accurate determination of the ultimate impulse leads to overly conservative safety boundaries or distorted evaluation indicators, thereby restricting the optimization of formation control parameters and fault tolerance design.

[0004] Currently, unmanned aerial vehicle network tasks are tending towards complex environments with high density, strong coupling, and coexistence of multiple faults. In order to accurately set control parameters, safety margins, and fault tolerances at the task planning stage, there is an urgent need for a new method that can automatically determine the ultimate impulse of the system and take into account model non-linearity and computational efficiency. Summary of the Invention

[0005] In view of the above deficiencies, the present invention proposes a method for determining the ultimate impulse of an unmanned aerial vehicle network system. This method takes into account model accuracy and simulation efficiency, and can quickly obtain transient tolerance boundaries in complex, non-linear, and multi-stage unmanned aerial vehicle networks, filling the gap in the existing technology for determining the ultimate impulse.

[0006] To achieve the above objectives, the present invention adopts the following technical solutions:

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

[0008] 1) Obtain the range identification flag flg1 of the interference intensity and the continuous characteristic identification flag flg2 of the interference intensity, and set the measurement times c;

[0009] 2) According to the logical combination of flg1 and flg2, use the interference intensity extraction module to determine a set of interference intensity data, and the interference intensity is sorted from large to small according to the absolute value of its perturbation to the system;

[0010] 3) Follow the principle of decreasing intensity and the principle of increasing step size, use the measurement module to conduct simulation experiments on each interference intensity, and iteratively measure the maximum action step size that keeps the system stable in the state dimension of the phase vector space;

[0011] 4) Input all the interference intensity data and the corresponding maximum action step size data into the linear characteristic judgment module to judge whether the absolute value of their reciprocals has a linear relationship;

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

[0013] Preferably, in step 2), the interference intensity extraction module first performs an "OR" logical operation on flg1 and flg2 to obtain a flag variable flg, and performs the following actions according to the value of flg: When flg = 1, it means that the interference intensity value is in 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. After simulating for 1 step, it is revoked. If the state of the system in the corresponding dimension can be restored to the stable domain, then retain this value, and repeat the above process until c effective 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 value is a finite discrete point. The module processing includes the following two cases: ① If the total number of discrete values cx ≤ c, traverse all discrete values and inject them into the system simulation model respectively. After simulating for 1 step, it is revoked. Retain a total of cy intensity values whose system state can be restored, and sort them from large to small according to the absolute value to form an interference intensity sequence; ② If cx > c, randomly extract interference intensity values from all discrete points and inject them into the system simulation model one by one. After simulating for 1 step, it is revoked. If the system state can be restored, then retain this value, and repeat until c valid values are obtained or all cx values have been traversed, and sort them from large to small according to the absolute value to form an interference intensity sequence.

[0014] Preferably, in step 3), the measurement process of the interference intensity data by the measurement module follows the following two principles:

[0015] Principle 1: During the whole measurement process, the interference intensity is injected into the network system model in the order of decreasing absolute value, and the limit impulse is measured sequentially;

[0016] Principle 2: When measuring a certain interference intensity data, the interference injection step size at this intensity is stepped in the order of increasing.

[0017] Preferably, in step 4), the linear characteristic judgment module judges whether there is a strong enough linear relationship between the absolute value of the reciprocal of the interference intensity data and its corresponding maximum step size data; if the linear relationship holds, the limit impulse generated by this interference in the phase vector space s i dimension is considered a fixed value; that is,

[0018] When the absolute value of the reciprocal of the interference intensity and the maximum action time in the phase space s i dimension have a linear relationship, there is ; where is the proportionality coefficient, which is a fixed constant;

[0019] Then there is , that is, the limit impulse corresponding to this external interference is equal to the proportionality coefficient , which is a fixed value, and the value of the limit impulse ;

[0020] On the contrary, there will be an interval range for the limit impulse, which needs further processing.

[0021] Preferably, the input of the linear characteristic judgment module in step 4) is two array data and , where

[0022] ,

[0023] ;

[0024] The output is a boolean variable, denoted as ，c and cy represents the number of interference intensity extractions, is the maximum action time in the phase space s i dimension;

[0025] When , it indicates that the linear relationship holds, and when , it indicates that the linear relationship does not hold; The condition is that there exists a fixed constant such that

[0026] ;

[0027] wherein, is the linear accuracy.

[0028] Preferably, the output of the detector in step 5) is the value of the limit impulse , which is jointly determined by the Boolean variable flg output by the interference intensity extractor and the Boolean variable output by the linear characteristic discriminator, that is:

[0029]

[0030] wherein, represents the x-th interference intensity extracted, c and cy represent the number of interference intensities extracted, represents the maximum step size of the x-th interference intensity output by the detector.

[0031] Furthermore, the present invention also provides a limit impulse measurement system for a UAV network system for implementing the above method, including:

[0032] a) An interference intensity extraction module for obtaining interference intensity data according to the control logics of flg1 and flg2;

[0033] b) A measurement module for iteratively simulating interference injection and measuring the maximum allowable injection step size at each intensity;

[0034] c) A linear characteristic judgment module for judging the linear relationship between the interference intensity and the maximum step size data;

[0035] d) An output module for calculating the limit impulse value according to the linear judgment result.

[0036] Preferably, the interference intensity extraction module includes a random sampling sub-module and a verification sub-module, and the latter is used to judge whether the state of the network system can be restored under the action of 1 simulation step; and / or, the measurement module includes an exponential growth sub-module and a binary search sub-module for improving the step size search efficiency and locating the maximum allowable step size.

[0037] 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 above method.

[0038] 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 above method is implemented.

[0039] Due to the adoption of the above technical solution, through collaborative design such as interference intensity extraction, two-stage step size search, and linear characteristic judgment, the present invention achieves the following remarkable technical effects:

[0040] 1. Quantify the transient tolerance boundary: By defining the product of the interference intensity and the continuous step size as the "limit impulse", the present invention establishes a unified and quantifiable evaluation index for the transient anti-interference ability of multi-UAV networks for the first time. The measured limit impulse value can be directly used as a hard constraint for formation robust control gain, safety redundancy design, and task scheduling, improving the pertinence and credibility of system design.

[0041] 2. Adaptive extraction considering both discrete and continuous interferences: According to the logical combination of the interference value range identifier (flg1) and the continuous characteristic identifier (flg2), the present invention realizes a unified sampling strategy for discrete faults (node disconnection, link interruption) and continuous disturbances (pulse wind field, acceleration mutation). The interference intensity data is injected into the system in the order of "decreasing intensity", ensuring the monotonic comparability of the experimental sequence and avoiding the sample redundancy and order sensitivity problems brought by traditional random-repeated experiments.

[0042] 3. Efficient two-stage step size search mechanism: Adopting a two-stage step size increasing strategy of "exponential growth lock area + binary approximation convergence", compared with the simple linear stepping or global Monte Carlo method, it can quickly locate the critical step size of system instability within the complexity of O(logN). This strategy significantly reduces the number of simulation iterations and reduces the computational overhead by more than 50%, meeting the online evaluation or fast offline calibration requirements of large-scale UAV networks.

[0043] 4. Automatic discrimination of linear characteristics and result refinement: Introducing a linear fitting residual evaluation mechanism between the absolute value of the reciprocal of the interference intensity and the maximum step size can automatically distinguish the linear limit (constant value) and the non-linear limit (interval). For linear scenarios, the system directly gives the average impulse constant value; for non-linear scenarios, it outputs the upper bound of the minimum impulse, avoiding the result distortion caused by artificial empirical thresholds and making the technical effects more reliable and applicable.

[0044] 5. Improve the model accuracy and generality: Using the phase point trajectory model to describe the multi-dimensional state evolution and coupling relationship of the UAV network, fully retaining the communication topology changes, task switching, and non-linear dynamic characteristics, the measured results are closer to the real operation boundary. The method has good portability for system scale, topology structure, and task mode, and can be directly embedded in different simulation platforms to achieve rapid deployment across scenarios and tasks.

[0045] 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.

[0046] 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.

[0047] 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

[0048] Figure 1 This is the basic structure diagram of the limit impulse meter.

[0049] Figure 2 Perform formation flying missions for drone network systems.

[0050] Figure 3 There are two stages of the UAV formation flight mission.

[0051] Figure 4 is the phase space of different task stages.

[0052] Figure 5 A diagram showing wind strength.

[0053] Figure 6 This is a graph showing stress intensity and maximum step length data. DETAILED DESCRIPTION

[0054] 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.

[0055] The purpose of the limit impulse measurement of the present invention is to find out the maximum impulse that the network system can withstand under a certain interference, so as to provide the interference injection boundary for the subsequent disturbance impulse injection experiment. After considering the differences in the types and value ranges of interference intensities, as well as the linear and non-linear performances of multiple rounds of measurement data, the present invention proposes a method for measuring the limit impulse using simulation experiments. The basic idea of this method is to first extract the intensity values of the interference according to the characteristics of the intensity, then conduct simulation experiments according to the principles of decreasing intensity and increasing step size, iteratively obtain the maximum step size data corresponding to each intensity value, then judge whether there is a strong linear relationship between the stress intensity data and the maximum step size data, and finally determine the final limit impulse value according to the judgment result. In order to improve the efficiency of the simulation experiment, the measurement method is designed as a limit impulse measurer, and its structure is as shown in Figure 1 shown.

[0056] In the following content, the input and output of the limit impulse measurer, as well as the implementation methods of its three core modules: the interference intensity extractor, the measurer, and the linear characteristic judge, will be elaborated in detail respectively.

[0057] I. Input and Output of the Measurer

[0058] (1) Input of the Measurer

[0059] The input of the limit impulse measurer includes the number of measurements c, the value range identifier of the interference intensity and the discrete characteristic identifier of the interference intensity . Their specific meanings are as described below.

[0060] The number of measurements c is an integer greater than 0. It is the number of rounds of the step size increasing measurement test, and also the number of groups of the input and output data of the measurer. In practice, the size of the number of measurements should be set by weighing two conflicting constraints: model accuracy and time cost. The default value of the present invention is .

[0061] The establishment of the value range identifier of the interference intensity is essentially a classification of the interference according to different value ranges of the intensity. It is a Boolean variable with a value of 0 or 1.

[0062] The establishment of the discrete characteristic identifier of the interference intensity is essentially a classification of the interference according to whether the intensity is a continuous variable. It is also a Boolean variable with a value of 0 or 1.

[0063] The values of the Boolean variables and and their corresponding meanings are shown in Table 1.

[0064] Table 1 and The value of

[0065]

[0066] (2)Output of the measuring device

[0067] The output of the measuring device is the value of the limiting impulse , specifically, it is the result of further processing of the interference intensity data and the maximum step size data. The processing method is determined by the Boolean variable output by the interference intensity extractor and the Boolean variable

[0068] (1.1)

[0069] Among them, represents the x-th interference intensity extracted, c and represent the number of interference intensity extractions, represents the maximum step size of the x-th interference intensity output by the measuring device.

[0070] Generally speaking, when , the output of the limiting impulse measuring device is the average value of the product of the interference intensity data and its corresponding maximum step size data; when, the limiting impulse takes the minimum value of the product of the absolute value of the interference intensity and the corresponding maximum measurement step size in the measurement data.

[0071] II. Core module of the measuring device

[0072] 1. Interference intensity extraction module

[0073] Two identification variables related to the interference intensity and After performing an OR logical operation, the marked variable is obtained. According to the different values of , the interference intensity extraction module will perform different actions.

[0074] (1)When , a value is randomly extracted within the value range of the interference intensity, and this value is used as the intensity value and injected into the network system model, and it is revoked after acting for 1 simulation step. If the network system phase point can return to the stable domain in the dimension of the phase vector space under the action of this impulse, that is, the state of the network system corresponding to the dimension can still be restored, then this value is retained, otherwise this value is discarded and randomly extracted again until such values are extracted, and they are arranged in descending order according to the absolute value and recorded as .

[0075] (2)When the interference intensity takes on a finite number of discrete points, and the number of these values is denoted as .

[0076] ① When all the discrete values are injected into the network system model as intensity values and then withdrawn after acting for 1 simulation step. Retain the values that the state of the network system corresponding to dimension can still recover. Suppose there are a total of such values, obtain the external stress intensity values as the measured input data, arrange them in descending order of absolute value, and denote them as .

[0077] ② 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 and then withdrawn after acting for 1 simulation step. If the state of the network system corresponding to dimension can still recover, retain this value; otherwise, discard this value and randomly select again until such values are obtained or all values are drawn. Arrange them in descending order of absolute value and denote them as or .

[0078] 2. Measurement module

[0079] To improve the execution efficiency of the measuring instrument, the measurement process of the interference intensity data follows the following two principles:

[0080] Principle 1 (Intensity decreasing principle) During the entire measurement process, the interference intensity is injected into the network system model in descending order of absolute value, and the limit impulse is measured sequentially.

[0081] Principle 2 (Step size increasing principle) When measuring a certain interference intensity data, the interference injection step size at this intensity advances in ascending order.

[0082] 3. Linear characteristic judgment module

[0083] The function of the linear characteristic judgment module is to judge whether there is a strong enough linear relationship between the absolute value of the reciprocal of the interference intensity data and its corresponding maximum step size data. If the linear relationship holds, the limit impulse generated by this interference in the dimension of the phase space can be considered a fixed value; otherwise, the limit impulse will have an interval range and further processing is required.

[0084] When the absolute value of the reciprocal of the interference intensity and the phase space Maximum action time in 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 the external interference is equal to the proportionality coefficient , which is a fixed value.

[0085] The input of the linear characteristic judgment module is two array data and , among which,

[0086] (1.2),

[0087] (1.3);

[0088] 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: there exists a fixed constant

[0089] (1.4);

[0090] Among them, is the linear accuracy, and the present invention adopts the default value .

[0091] 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 shown in Figure 2 . The network system is composed of 9 homogeneous rotary-wing UAVs, and it is required to complete the formation of a "square" formation and maintain this formation to fly along the planned trajectory.

[0092] The multi-UAV network system adopts the "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 obtain the position information of the adjacent first-level followers at the same time and maintain the corresponding relative distance. Under the above strategy, the multi-UAV network system realizes integral formation flight.

[0093] The flight mission of the UAV formation can be divided into two stages, namely the formation stage and the formation flight stage.

[0094] Stage 1: Formation stage

[0095] The initial position coordinates of each UAV in the takeoff 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.

[0096] The UAVs that arrive at the formation position in advance will hover and wait until all 9 UAVs reach the designated formation position, and the entire cluster will execute the flight mission of the second stage.

[0097] Stage 2: Formation flight stage

[0098] 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, and the entire cluster will complete the task of flying along the planned path in the "square" formation.

[0099] The two stages of the mission are as Figure 3 shown. The most important task 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 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 2.

[0100] Table 2 State analysis table of UAV formation network

[0101]

[0102] In the first stage of the mission, the UAV formation network needs to complete the formation mission, and only the formation state of the network needs to be concerned at this time; 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, the formation state and the position state of the network need to be concerned at the same time. Therefore, in the formation stage, 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 stage, 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 .

[0103] The present invention analyzes the types of interference that the case UAV network has to face from two aspects: the self - faults of the UAV formation network and external environmental factors, and gives a quantitative expression of the interference intensity.

[0104] (1)Identify the types of interference

[0105] According to the results of the state analysis, the formation state and position state of the UAV formation network are the network states that need to be concerned about. According to the UAV formation network operation rules listed in Table 2, the normal communication between each UAV is the basis for ensuring the stability of the formation and position of the network system. Once a communication fault occurs in a certain UAV and it cannot obtain and send information, the formation state and position state of the entire UAV formation will definitely be affected. Therefore, the communication fault of a single UAV is the first type of interference that the case UAV formation has to bear.

[0106] In addition, the UAV formation network will be affected by 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 that the case UAV formation bears.

[0107] (2)Define the interference intensity

[0108] Interference one: Communication fault of a single UAV

[0109] Any of the 9 UAVs in the case UAV formation network may have a communication fault. However, due to the connection relationship of the formation structure, the degree of impact on the formation state and position state of the network system caused by the communication faults of different UAVs is different. For example, during the formation flight stage, if UAV No. 1 has a communication fault, 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 communication fault occurs in UAV No. 6, only itself cannot find the flight direction for the next moment, and other UAVs are not affected. In fact, the interference intensity caused by the communication fault of a UAV is related to the number of UAVs connected to it.

[0110] 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 fault intensity here. In this case, the concept of node degree is used to provide a quantitative expression for the intensity of the communication fault.

[0111] Denote the degree of UAV as , and the number of the UAV with a communication fault at a certain moment is recorded in the set . Then the intensity of this interference of the communication fault at this moment is:

[0112] (1.5);

[0113] As can be seen from Equation (1.5), the communication fault intensity is a finite number of values within the interval . For example, when communication faults occur simultaneously in UAV No. 1 and UAV No. 9, the communication fault intensity suffered by the UAV formation network is:

[0114] (1.6);

[0115] In particular, indicates that no UAV has a communication fault; indicates that all UAVs have communication faults simultaneously.

[0116] Interference 2: Wind force

[0117] In this case, the intensity of the wind force is described using simplified Newtonian forces, denoted as , and it is assumed that this wind force acts on 9 UAVs simultaneously.

[0118] is a vector, and its component form is denoted as , representing the wind force intensity in the direction and the direction respectively, as shown in Figure 5 . The magnitude of the wind force intensity is described using the 2-norm of the vector, i.e., .

[0119] The following gives a detailed description of measuring the limit impulse of the communication fault interference of a single UAV according to the above method.

[0120] (1) Analysis of the interference intensity value range identification

[0121] According to the interference analysis results, the value range of the communication fault intensity of a single UAV is , so the interference intensity value range identification .

[0122] Each UAV's communication status has two values, "fault" and "normal". Therefore, there are a total of modes of UAV communication faults for 9 UAVs. According to Equation (1.5), it is calculated that these 512 modes can generate a total of 23 different discrete stress intensity values, which are arranged in ascending order in Table 3. Therefore, the continuous characteristic identification of the external stress intensity .

[0123] (2) Interference intensity extraction

[0124] In this case, the number of measurement times is set. First, mark the variable , secondly, although the number of value-taking , but the number of communication fault modes , therefore, corresponding to case (2) ② in step two, randomly select from the 512 communication fault modes corresponding to the 23 strength values in Table 3, put them into the model for 1-step injection test. If the simulation model can be restored, retain this value until 24 strength value-takings are obtained.

[0125] This case has special circumstances. Since each interference strength value corresponds to multiple communication fault modes, after randomly selecting a certain interference strength value, it is necessary to randomly select the communication fault modes under this strength again. For example, there are 4 communication fault modes corresponding to the strength of 0.0833, as shown in Table 4. If the strength value selected in the first-layer random selection is 0.0833, a second-layer random selection is still required to determine the injection mode of this strength interference.

[0126] Table 3 23 value-takings of the communication fault strength of a single unmanned aerial vehicle and the corresponding number of fault modes

[0127]

[0128] Table 4 4 communication fault modes with the stress strength of 0.0833

[0129]

[0130] After extraction and 1-step injection detection, 24 groups of communication fault modes and interference strengths are determined as the input of the next detector, as shown in Table 5.

[0131] (3) Determination results of the ultimate impulse

[0132] According to the method in the present invention, the ultimate impulse of the 24 groups of communication fault interferences in Table 5 is determined. It is found in the determination process that the communication fault interference of a single unmanned aerial vehicle only affects the formation deformation amount and has no effect on the position offset amount. Therefore, the ultimate impulse of the communication fault interference of a single unmanned aerial vehicle in the phase vector space dimension (the dimension representing the position offset amount) is , and the determination results in the phase vector space dimension (the dimension representing the formation deformation amount) are listed in Table 5.

[0133] Table 5 24 groups of communication fault stresses and their corresponding determination results of the ultimate impulse

[0134]

[0135] (4) Linear characteristic analysis

[0136] The relationship between the absolute value of the reciprocal of the interference intensity and the maximum step size in Table 5 is plotted as shown in Figure 6 the scatter plot. It can be seen that the 24 groups of data show obvious clustering characteristics, do not have linear characteristics, nor do they have functional characteristics.

[0137] It should be further noted that there are two pairs of data in Table 5 that are special: ① The 18th and 19th groups of data show that due to differences in communication failure modes, it is possible to measure different ultimate impulses under the same interference intensity; ② The 22nd and 23rd groups of data show that there is no completely monotonic relationship between the interference intensity and the maximum action step size, that is, it is possible that when the interference intensity is relatively large, its maximum action step size is also large.

[0138] The reasons for the above two phenomena are analyzed as follows:

[0139] The definition of the interference intensity of a single UAV communication failure is actually the degree of influence of a single UAV failure on the connectivity performance of the cluster network. There is no direct functional relationship between the connectivity performance of the formation network and the deformation amount of the formation shape. Due to the dynamic characteristics of the UAV formation network, during the movement of the network system, it may "accidentally" reduce or expand the influence degree of the loss of connection between some single UAVs on the formation shape. For example, although the communication failure mode corresponding to the 23rd group of data is that only UAV No. 2 loses contact with the cluster, since UAVs No. 6 and No. 7 need to connect to UAV No. 2 to accurately follow, this failure mode actually affects 3 UAVs. In contrast, the communication failure mode corresponding to the 22nd group of data is the failure of UAVs No. 6 and No. 8, and only 2 UAVs are affected. In addition, UAV No. 8 is in the front row of the formation. When it loses contact with other members of the network, it will continue to fly at the original flight speed, which accidentally keeps in line with the overall flight of the formation in the x direction, reducing the influence caused by the loss of connection on the formation shape.

[0140] (5)Calculate the measurement results

[0141] Since the data does not have linear characteristics, according to the third fraction of Equation (1.1), the ultimate impulse of the single UAV communication failure interference in the phase vector space dimension (the dimension representing the deformation amount of the formation shape) is the minimum value among all the data in the last column of Table 5, that is

[0142] Stage 1: I CFlim 1 = 89.0477 (1.7), Based on the above analysis and measurement process, the measurement result of the ultimate impulse of the single UAV communication failure interference in Stage 2 is: Stage 2: I 2 CFlim =[I CFlim 1 ,ICFlim 2 =[89.0477, ∞] (1.8); where, I CFlim 1 is the limit impulse of the communication fault interference stress in the s1 dimension of the phase vector space, and I CFlim 2 is the limit impulse of the communication fault interference stress in the s2 dimension of the phase vector space, and I 2 CFlim is the limit impulse vector of the communication fault interference stress in the phase vector space.

[0143] (6) Analysis of the limit impulse of natural wind interference

[0144] According to Figure 5 the given wind force model, the 9 drones in the drone formation network will generate the same acceleration under the action of natural wind. Also, because the 9 drones are isomorphic, the overall network will deviate from its original position at the same speed. It can be inferred from this that when the wind force acts continuously, it will basically not cause changes in the deformation of the network formation. When the wind force is withdrawn, the grouping rules and path tracking rules of the drones will cause each drone to move towards the established target position. At this time, the formation of the cluster will change due to the different movement directions, speeds, and accelerations of each drone. However, this change will also gradually recover as the drone formation returns to the target position.

[0145] Under the continuous action of the wind force, the drone formation network will continuously deviate from the target position. However, according to the grouping rules and path tracking rules, no matter how far the formation deviates from the target position, once the wind force stops acting, the network system will return to the target position under the control of the movement rules. In other words, as long as the impulse of the natural wind interference is a finite value, the position offset of the network will always recover to a very small state.

[0146] The above analysis shows that the interference of natural wind on the phase point trajectory in the first stage is extremely small, and the changes in the phase point trajectory in the second stage caused by the natural wind with a finite impulse will always recover as the interference stops. Therefore, the limit impulse of natural wind on the network in both stages is infinite, denoted as:

[0147] First stage: I Wlim = ∞; Second stage: I 2 Wlim = [I Wlim 1 , I Wlim 2 = [∞, ∞] (1.9); where, I Wlim is the limit impulse of the natural wind stress in the first stage, and I 2 Wlim is the limit impulse vector of the natural wind stress in the second stage, and I Wlim 1I is the limit impulse of the natural wind stress in the s1 dimension of the phase vector space. Wlim 2 I is the limit impulse of the natural wind stress in the s2 dimension of the phase vector space.

[0148] The reasons for the infinite limit impulse here are mainly the following three aspects:

[0149] (1) The wind force model assumes that the external force generated by the wind on each drone is uniform.

[0150] (2) The nine drones are considered to have ideal equal masses.

[0151] (3) Without constraints such as spatial boundaries and the maximum deviation distance of the drones, it is assumed that the drones can always obtain the target position information.

[0152] Under these three strong assumptions, the drones in the case can always maintain the formation and return to the target position, and the natural wind interference will not cause the divergence of the network state of the drone formation.

[0153] 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.

[0154] 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 storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0155] The present application is described with reference to the flowcharts and / or block diagrams of 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, and 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 the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 each process or multiple processes and / or blocksFigure 1 means for the functions specified in one or more boxes.

[0156] 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 particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements the functions specified in one Figure 1 process or a plurality of processes and / or boxes Figure 1 or more boxes.

[0157] These computer program instructions may also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 process or a plurality of processes and / or boxes Figure 1 or more boxes.

[0158] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0159] 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). Memory is an example of computer-readable media.

[0160] Computer-readable media includes 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 disc (DVD) or other optical storage, magnetic cassettes, magnetic tape 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 media such as modulated data signals and carrier waves.

Claims

1. A method for determining the ultimate impulse of a drone network system, characterized in that, The method includes the following steps: 1) Obtain the range identifier flg1 of the interference intensity and the continuous characteristic identifier flg2 of the interference intensity, and set the number of measurements c; 2) According to the logical combination of flg1 and flg2, use the interference intensity extraction module to determine a set of interference intensity data, and the interference intensity is sorted from large to small according to the absolute value of its perturbation to the system; 3) Following the principle of decreasing intensity and the principle of increasing step size, use the measurement module to perform simulation experiments on each interference intensity, and iteratively measure the maximum action step size that keeps the system stable in the state dimension of the phase vector space; 4) Input all the interference intensity data and the corresponding maximum action step size data into the linear characteristic judgment module to judge whether the absolute value of their reciprocals has a linear relationship; 5) If the linear relationship holds, output the average value of the product of each interference intensity and the maximum step size as the limit impulse; if not, output the product of the minimum interference intensity and the maximum step size as the limit impulse value.

2. The method according to claim 1, wherein In step 2), the interference intensity extraction module first performs an "OR" logical operation on flg1 and flg2 to obtain a flag variable flg, and performs the following actions according to the value of flg: When flg = 1, it means that the interference intensity value is in 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. After simulating for 1 step, it is withdrawn. If the state of the system in the corresponding dimension can be restored to the stable domain, the value is retained. Repeat the above process until c valid intensity values are obtained, and sort the retained values from large to small according to the absolute value to form an interference intensity sequence; When flg = 0, it means that the interference intensity value is a finite discrete point. The module processing includes the following two cases: ① If the total number of discrete values cx ≤ c, traverse all discrete values and inject them into the system simulation model respectively. After simulating for 1 step, withdraw them. Retain a total of cy intensity values whose system state can be restored, and sort them from large to small according to the absolute value to form an interference intensity sequence; ② If cx > c, randomly extract interference intensity values from all discrete points and inject them into the system simulation model one by one. After simulating for 1 step, withdraw them. If the system state can be restored, retain the value. Repeat until c valid values are obtained or all cx values have been traversed, and sort them from large to small according to the absolute value to form an interference intensity sequence.

3. The method according to claim 1, characterized in that, In step 3), the measurement process of the measurement module for the interference intensity data follows the following two principles: Principle 1: During the whole measurement process, the interference intensity is injected into the network system model in the order of decreasing absolute value, and the limit impulse is measured in turn; Principle 2: When measuring a certain interference intensity data, the interference injection step size at this intensity is stepped in the order of increasing from small to large.

4. The method according to claim 2, wherein The linear characteristic determination module in step 4) determines whether there is a strong enough linear relationship between the absolute value of the reciprocal of the interference intensity data and its corresponding maximum step size data; if the linear relationship holds, the limit impulse generated by the interference in the phase vector space s i dimension is considered a fixed value; that is, When the absolute value of the reciprocal of the interference intensity σ and the maximum action time in the i dimension of the phase space s have a linear relationship, there is ; Among them, k i is a proportionality coefficient and is a fixed constant; Then there is , that is, the limit impulse corresponding to the external interference is equal to the proportionality coefficient k i , which is a fixed value, and the value of the limit impulse ; On the contrary, there will be an interval range for the limit impulse, which needs further processing.

5. The method according to claim 4, wherein The input of the linear characteristic judgment module in step 4) is two array data X 1 and X 2 , where ， ； The output is a Boolean variable, denoted as , c and cy represents the number of interference intensity extractions, is the maximum action time in the phase space s i dimension; 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 k such that ； where ε is the linear precision, represents the maximum step size of the x-th interference intensity output by the detector.

6. The method according to claim 5, characterized in that The output of the measuring device in step 5) is the value of the ultimate impulse , which is jointly determined by the Boolean variable flg output by the interference intensity extractor and the Boolean variable output by the linear characteristic discriminator, that is: ， Among them, represents the x-th interference intensity extracted.

7. An extreme impulse measurement system for a drone network system, characterized in that, For implementing the method according to any one of claims 1-6, including: a) an interference intensity extraction module for obtaining interference intensity data according to the control logic of flg1 and flg2; b) a measurement module for iteratively simulating interference injection and measuring the maximum allowable injection step size at each intensity; c) a linear characteristic judgment module for judging the linear relationship between the interference intensity and the maximum step size data; d) an output module for calculating the limit impulse value according to the linear judgment result.

8. The system according to claim 7, wherein The interference intensity extraction module includes a random sampling sub-module and a verification sub-module, and the latter is used to judge whether the network system state can be restored under the action of 1 simulation step; and / or, the measurement module includes an exponential growth sub-module and a binary search sub-module for improving the step size search efficiency and locating the maximum allowable step size.

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 1-6.

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 1-6 is implemented.

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