An Impact Load Monitoring Method and System for UAV Anti-Collision

Abstract

The present invention relates to the technical field of unmanned aerial vehicle (UAV) impact load monitoring, and particularly relates to an impact load monitoring method and system for UAV anti-collision, including: obtaining the impact load of the UAV in each group of experiments and the data of three flight elements of the UAV through intelligent sensors; calculating the incremental influence degree of each flight element and the synchronization consistency of the impact load; analyzing the deviation between the correlation vector of each flight element and the total correlation vector to obtain the contribution degree of each flight element to the impact load, so as to calculate the adaptive fitting weight of each flight element; and combining the data of each flight element and the UAV impact load data to fit a fitting function between the UAV impact load and the three flight elements, so as to obtain the maximum impact load, and using an optimization algorithm to obtain the optimal value of the UAV airbag parameters. The present invention can effectively reduce the impact load of the UAV and ensure the safety and effectiveness of the UAV during collision.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) impact load monitoring, and particularly to an impact load monitoring method and system for UAV anti-collision. Background Art

[0002] In recent years, with the wide application of UAVs in various industries, due to the limitations of the UAVs' own performance and the professional operation of the operators, especially when the UAV is performing inspections and facing the influence of complex external environments, when the active obstacle avoidance function of the UAV system is restricted or fails, the UAV often has passive collision accidents with structures, resulting in damage to the UAV and even being unable to be used. From the perspective of passive anti-collision, equipping the UAV with safety protection devices such as protective covers and UAV airbags has become the focus of attention in the field of UAV passive anti-collision.

[0003] In the process of developing UAV airbags, it is necessary to determine the optimal design parameters of the airbags according to the actual impact load of the UAV. Therefore, the detection of the impact load for UAV anti-collision is crucial. Since the state of the UAV when it fails in the air is complex and variable, it makes the measurement of the impact load difficult. In traditional measurements, the impact load is mainly related to the flight altitude, environmental wind speed, and failure flight speed of the UAV. By simulating measurements multiple times and using polynomial fitting, the coupling relationship between the impact load and the three flight elements (flight altitude, environmental wind speed, and failure flight speed) is obtained. However, in the actual environment, the coupling relationship between the three flight elements and the impact load is non-linear, and the influence degrees of the three flight elements on the impact load are different. Therefore, in the traditional method, equal fitting weights are assigned to each flight element, resulting in a deviation between the fitted impact load and the actual impact load, affecting the effectiveness of airbag design. Summary of the Invention

[0004] In order to solve the technical problem that the fixed threshold value in the above-mentioned rotating door algorithm affects the accuracy of monitoring aquaculture nutrition and animal health protection data, the purpose of the present invention is to provide an impact load monitoring method and system for UAV anti-collision. The technical solutions adopted by the method are specifically as follows:

[0005] Obtain the impact load of the UAV in each group of experiments and the data of the three flight elements of the UAV through intelligent sensors;

[0006] Based on the data differences of each flight element in all experiments and the variation of the UAV impact load, determine the variation influence degree of each flight element, and combine the difference degree between the data trend distribution of each flight element and the trend distribution of the UAV impact load to obtain the impact load synchronization consistency of each flight element.

[0007] According to the data of each flight element and the impact load data of the UAV, obtain the correlation vectors between each flight element and the impact load, as well as the total correlation vector between the three flight elements and the impact load. Analyze the deviation between the correlation vector of each flight element and the total correlation vector to obtain the contribution degree of each flight element to the impact load; calculate the adaptive fitting weights of each flight element in combination with the contribution degrees of the three flight elements to the impact load.

[0008] Based on the adaptive fitting weights of each flight element, and in combination with the data of each flight element and the UAV impact load data, fit the fitting function between the UAV impact load and the three flight elements to obtain the maximum impact load, and use the optimization algorithm to obtain the optimal value of the UAV airbag parameters.

[0009] Preferably, the flight elements include flight altitude, environmental wind speed, and failure flight speed.

[0010] Preferably, the calculation formula for the incremental influence degree of each flight element is:

[0011] In the formula, B represents the incremental influence degree of the current flight element, N represents the number of data, ΔF i represents the absolute value of the difference between the i-th and the (i + 1)-th data values after sorting the data of the current flight element, ΔQ i represents the absolute value of the difference between the i-th and the (i + 1)-th impact loads after sorting the UAV impact loads, and α is a constant to avoid the denominator being zero. Among them, the sorting is to arrange the data values of the current flight element and the UAV impact load in ascending order in all experiments.

[0012] Preferably, the calculation formula for the impact load synchronization consistency of each flight element is: In the formula, C represents the impact load synchronization consistency of the current flight element respectively, ρ x and ρ Q represent the trend strengths of the current flight element and the UAV impact load respectively. Among them, the trend strength is calculated by using the time series decomposition algorithm after sorting.

[0013] Preferably, the process of obtaining the correlation vectors between each flight element and the impact load, as well as the total correlation vector between the three flight elements and the impact load is:

[0014] Arrange the data of each flight element in ascending order to obtain the data sequence of each flight element, and arrange the UAV impact loads in ascending order to form the UAV impact load sequence.

[0015] Each correlation vector between each flight element and the impact load is composed of elements at the same positions in the data sequences of each flight element and the UAV impact load sequence; each total correlation vector between the three flight elements and the impact load is composed of elements at the same positions in the data sequences of all flight elements and the UAV impact load sequence.

[0016] Preferably, the calculation formula for the contribution degree of each flight element to the impact load is:

[0017] In the formula, D represents the contribution degree of the current flight element to the impact load, C represents the impact load synchronization consistency of the current flight element, N represents the number of data, and θ(y i ,y i+1 ) represents the included angle between the i-th and the (i + 1)-th correlation vectors between the current flight element and the impact load, and θ(z i ,z i+1 ) represents the included angle between the i-th and the (i + 1)-th total correlation vectors between the three flight elements and the impact load, and α is a constant to avoid the denominator being zero.

[0018] Preferably, the calculation formula for the adaptive fitting weight of each flight element is: In the formula, w h represents the adaptive fitting weight of the flight altitude, D h , D v and D f respectively represent the contribution degree of the flight altitude to the impact load, the contribution degree of the environmental wind speed to the impact load, and the contribution degree of the time-effective flight speed to the impact load.

[0019] Preferably, the acquisition of the fitting function between the UAV impact load and the three flight elements includes:

[0020] Combining the UAV impact loads and the data of each flight element in all groups of experiments, and using the polynomial fitting algorithm to fit the fitting function between the UAV impact load and the three flight elements with the adaptive fitting weights of each flight element, where the independent variables in the fitting function are the three flight elements and the dependent variable is the UAV impact load.

[0021] Preferably, the acquisition of the optimal values of the UAV airbag parameters includes:

[0022] Obtaining the response fitting function of the UAV maximum impact load according to the relationship between the maximum impact load and the UAV airbag design parameters, and using the genetic optimization algorithm to obtain the optimal values of each airbag parameter of the UAV.

[0023] On the other hand, an embodiment of the present application provides an impact load monitoring system for UAV anti-collision, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0024] The present invention has the following beneficial effects:

[0025] In the present invention, through the method of uniform experiment, the intelligent sensor is used to obtain the experimental data of the UAV flight parameters and the UAV impact load in the experimental group. Based on the variation and distribution trend of a single flight element and the impact load, the synchronization consistency of the current flight element is obtained, and the influence of the current flight element on the impact load is measured. Based on the deviation between the correlation vector of a single flight element and the total correlation vector of the three flight elements, the contribution degree of the single flight element is obtained, reflecting the contribution degree of the current flight element to the impact load under the combined action of the three flight elements. Thus, the fitting weight of the flight element is obtained. Substituting the data and the fitting weight into the fitting algorithm, the fitting relationship between the flight parameters and the impact load is finally obtained, and the UAV impact load close to the accurate value is obtained. Based on the UAV impact load, the airbag parameters in the anti-collision airbag for reducing the UAV load are obtained by using finite element analysis and genetic algorithm. Since the influence degree of a single flight element and the correlation situation of multiple flight elements are comprehensively considered in the present invention to obtain the fitting weight of a single flight element, the finally obtained UAV impact load is closer to the actual impact load, and the finally designed airbag parameters can effectively reduce the impact load of the UAV, ensuring the safety and effectiveness of the UAV during collision. Description of the Drawings

[0026] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0027] Figure 1 It is a flowchart of an impact load monitoring method for UAV anti-collision provided by an embodiment of the present invention. Detailed Embodiments

[0028] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes, in conjunction with the accompanying drawings and preferred embodiments, a method and system for impact load monitoring for UAV anti-collision according to the present invention, including its specific implementation manner, structure, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0030] The following specifically describes the specific solution of a method for impact load monitoring for UAV anti-collision provided by the present invention in conjunction with the accompanying drawings.

[0031] Please refer to Figure 1 , which shows a flowchart of a method and system for impact load monitoring for UAV anti-collision provided by an embodiment of the present invention. The method includes the following steps:

[0032] Step 100: Obtain the impact load of the UAV in each group of experiments and the data of three flight elements of the UAV through intelligent sensors.

[0033] To better design the parameters of the UAV's airbag, it is necessary to strictly measure the impact load between the UAV and the ground when it fails. In this embodiment, the range information of the UAV flight parameters is obtained through the UAV's flight manual. The flight altitude refers to the altitude range that the UAV can fly, the environmental wind speed refers to the wind speed range that the UAV can tolerate, and the flight speed at failure refers to the flight speed range of the UAV. In this embodiment, the UAV flight elements include flight altitude, environmental wind speed, and failure flight speed. In this embodiment, the selected range of flight altitude h is 0 - 200m, the range of environmental wind speed v is 0 - 12m / s, and the range of failure flight speed f is 0 - 20m / s.

[0034] To reduce the cost of the experiment, this embodiment adopts the method of uniform experiment to reduce the number of experiments, and uses a model of the same size and mass as the UAV during the test, and uses intelligent sensors to obtain the flight parameters of the UAV when it fails. The intelligent sensors include a height sensor, a wind speed sensor, a speed sensor, and a collision impact sensor. In this embodiment, the number of groups of the uniform experiment is set to 200 groups. For each group of experimental information, it includes the impact load of the UAV and the data of each flight element.

[0035] To reduce the influence of the sizes and units of different parameter information, normalization operations are performed on 200 sets of experimental data obtained from experiments to obtain the normalized parameter information. It should be noted that there are many methods for normalization operations, and implementers can select them by themselves in actual application scenarios. In this embodiment, the Z-Score normalization method is used to normalize the data of each parameter. The specific normalization process is prior art and will not be elaborated in this embodiment.

[0036] Step 200: Based on the data differences of each flight element in all experiments and the changing situation of the impact load of the UAV, determine the changing influence degree of each flight element, and combine the difference degree between the data trend distribution of each flight element and the trend distribution of the UAV impact load to obtain the impact load synchronization consistency of each flight element.

[0037] When the UAV fails in the air, the impact load between the UAV and the ground is closely related to the flight information of the UAV, that is, the impact load is related to the flight height, environmental wind speed, and failure flight speed. Among them, the flight height determines the normal falling duration after the UAV fails, and often shows a positive correlation with the impact load; the environmental wind speed affects the air resistance when the UAV falls, but it will affect the falling attitude of the UAV and indirectly affect the size of the impact load; the failure flight speed determines the initial kinetic energy of the UAV, and generally, the greater the initial kinetic energy, the greater the impact load during impact. The three flight elements jointly affect the impact load of the UAV, and the interaction relationship among them is complex and the influence degrees on the impact load are different. Therefore, further analysis is required based on the monitoring data obtained by the intelligent sensors of the experimental group.

[0038] In this embodiment, the flight height h, environmental wind speed v, and failure flight speed f of the three flight elements of the UAV are used as independent variables, and the impact load acting on the UAV among the three flight elements, so the impact load is used as the dependent variable. To analyze the influence of the three flight elements on the impact load, take any one of the flight elements as an example for detailed elaboration to analyze the impact load synchronization consistency of each flight element. The specific process is as follows:

[0039] A single flight element is a continuous space within the value range. Through the method of uniform experiments, 200 sets of experimental data are sampled. Thus, when the value of a single flight element changes, if the change amount of this flight element is larger, and at the same time the change amount of the corresponding impact load is also larger, it indicates that the influence of this flight element on the impact load is greater.

[0040] Therefore, for the current flight element, the values in the 200 groups of experimental data of the current flight element are sorted in ascending order. Based on the above analysis, by measuring the change relationship between the flight element and the impact load, the progressive influence degree B is obtained. In this embodiment, based on the data differences of each flight element in all experiments and the progressive situation of the UAV impact load, the progressive influence degree of each flight element is determined. The specific calculation formula is as follows:

[0041] In the formula, B represents the progressive influence degree of the current flight element, N represents the number of data, ΔF i represents the absolute value of the difference between the i-th and the (i + 1)-th data values after sorting the data of the current flight element, and ΔQ i represents the absolute value of the difference between the i-th and the (i + 1)-th impact loads after sorting the UAV impact loads. Among them, the sorting is to sort the data values of the current flight element and the UAV impact loads in all experiments in ascending order. α is a constant to avoid the denominator being zero. In this embodiment, α = 0.01 is set. It should be noted that the current flight element is any one of the three flight elements {h, v, f}.

[0042] Repeating the above process can obtain the progressive influence degree of each flight element. It can be understood from the above process that if the data values of two adjacent groups of the current flight element can cause a large change in the impact load under the condition of small change, that is is much greater than 1, then the influence degree of this flight element on the impact load is higher, and at this time, the value of the progressive influence degree obtained is larger. On the contrary, if the action degree of this flight element on the impact load is smaller, and is much less than 1, that is, the value of the progressive influence degree is smaller.

[0043] The progressive influence degree is mainly measured by the progressive situation of the values between the current flight element and the UAV impact load. At the same time, the influence degree of the flight element is closely related to the overall trend of the impact load data. That is, if the trend change of the current flight element is more consistent with the trend of the impact load, it indicates that the connection degree between the two is higher. In order to measure the trend situation of the two, the STL (Seasonal-Trend Decomposition using LOESS) time series decomposition is performed on the two sequences obtained by sorting the data of the current flight element and the UAV impact load data in ascending order respectively. According to the trend terms of each sequence, using the trend intensity calculation formula, the trend distribution situations of the current flight element data and the UAV impact load are obtained respectively.

[0044] Based on the above analysis, according to the progressive influence degree of each flight element, combined with the difference degree between the data trend distribution of each flight element and the trend distribution of the UAV impact load, the impact load synchronization consistency of each flight element is obtained. The corresponding calculation formula in this embodiment is as follows:

[0045] In the formula, C represents the impact load synchronization consistency of the current flight element, B represents the progressive influence degree of the current flight element, and ρ x and ρ Q respectively represent the trend strengths of the current flight element and the impact load of the UAV.

[0046] Therefore, if the trends of the current flight element and the UAV impact load are more consistent in the overall data distribution, that is, the trend strength ρ x of the current flight element and the trend strength ρ Q of the UAV impact load are closer in value, the value of the impact load synchronization consistency C of the current flight element is larger, indicating that the change of the current flight element has a greater impact on the UAV impact load.

[0047] Step 300: According to the data of each flight element and the data of each impact load of the UAV, obtain the correlation vectors between each flight element and the impact load and the total correlation vector between the three flight elements and the impact load, analyze the deviation between the correlation vector of each flight element and the total correlation vector, and obtain the contribution degree of each flight element to the impact load; calculate the adaptive fitting weights of each flight element in combination with the contribution degrees of the three flight elements to the impact load.

[0048] Through step 200, the impact load synchronization consistency of each flight element on the UAV impact load can be obtained, which measures the influence degree of the current flight element on the impact load. However, since the data of the experimental group is sampled within the continuous value range of the three flight elements through a uniform experiment, the sampling of the same flight element is not evenly distributed. The impact load of each group of data is the result of the combined action of the three flight elements, and the synchronization consistency only considers from the perspective of a single flight element and does not measure the influence of the three flight elements from a comprehensive perspective, which may cause deviations in the measurement of the importance degree. Therefore, further analysis is required in combination with the data of the experimental group.

[0049] Since in a uniform experiment, when the value of a single flight element changes, the values of other flight elements also change. Therefore, it is necessary to measure the contribution of a single flight element to the UAV impact load according to the change degree of the flight element. Therefore, in this embodiment, the data of each flight element are respectively sorted in ascending order to obtain the data sequence of each flight element, and the UAV impact load is sorted in ascending order to form the UAV impact load sequence. Taking a single flight element as an example, in this embodiment, taking the flight altitude as an example, the data values of the flight altitude in all experiments are sorted in ascending order to form the data sequence of the flight altitude, and then each element at the corresponding position in the data sequence of the flight altitude and the UAV impact load sequence is combined to form each correlation vector between the flight altitude and the impact load, such as y i=(h i , Q i ), y i represents the correlation vector between the i-th flight altitude and the impact load. h i , Q i are respectively the i-th element (flight altitude) in the data sequence of flight altitude and the i-th element (unmanned aerial vehicle impact load) in the unmanned aerial vehicle impact load sequence. Further, data sequences of three flight elements are obtained, and by combining the data at each position in the three data sequences with the unmanned aerial vehicle impact load at the same position in the unmanned aerial vehicle impact load sequence, each total correlation vector between the three flight elements and the impact load is constructed. For example, z i =(h i , v i , f i , Q i ), z i represents the total correlation vector between the i-th three flight elements and the impact load. v i , f i are respectively the i-th elements in the data sequence of environmental wind speed and the data sequence of failure flight speed.

[0050] If the angle between the correlation vectors of a single flight element between two adjacent experimental groups is closer to the angle between the total correlation vectors of the three flight elements, it indicates that the change in the impact load between these two experimental groups is more contributed by the flight altitude. That is, if the angle of θ(y i , y i+1 ) is closer to the angle of θ(z i , z i+1 ), it indicates that the contribution degree of the flight altitude to the impact load is higher.

[0051] Therefore, in this embodiment, by analyzing the deviation between the correlation vectors of each flight element and the total correlation vector, the contribution degree of each flight element to the impact load is obtained. The specific calculation formula is:

[0052] In the formula, D represents the contribution degree of the current flight element to the impact load, C represents the impact load synchronization consistency of the current flight element, N represents the number of data, θ(y i , y i+1 ) represents the angle between the correlation vectors of the i-th and the i + 1-th current flight element and the impact load, θ(z i , z i+1 ) represents the angle between the total correlation vectors of the i-th and the i + 1-th three flight elements and the impact load, and α is a constant to avoid the denominator being zero.

[0053] For each flight element, if the angle between the associated vector of a single flight element and the total associated vector of the three flight elements is closer in each adjacent pair of experimental groups, it indicates that the influence of the other two flight elements on the impact load of the UAV is smaller, reflecting that the current flight element, i.e., flight speed, has a greater influence on the impact load.

[0054] Obtain the contribution degree D of flight altitude to the impact load in the same way as above h and the contribution degree D of environmental wind speed to the impact load v and the contribution degree D of aging flight speed to the impact load f . Further, analyze the proportion of the contribution degree of a single flight element to the impact load in the sum of the contribution degrees of the three flight elements to the impact load to obtain the adaptive fitting weight of the single flight element. Taking flight altitude as an example, the expression of the adaptive fitting weight is:

[0055] In the formula, w h represents the adaptive fitting weight of flight altitude, D h , D v and D f represent the contribution degree of flight altitude to the impact load, the contribution degree of environmental wind speed to the impact load, and the contribution degree of aging flight speed to the impact load, respectively.

[0056] If the contribution degree of flight altitude to the impact load obtained finally is greater and the proportion in the contribution degrees of the three flight elements is higher, then the value of the adaptive fitting weight of flight altitude is greater, indicating that the importance degree of flight altitude in data fitting is higher.

[0057] Step 400: Based on the adaptive fitting weights of each flight element, combined with the data of each flight element and the UAV impact load data, fit the fitting function between the UAV impact load and the three flight elements to obtain the maximum impact load, and use the optimization algorithm to obtain the optimal value of the UAV airbag parameters.

[0058] Based on the process of step 300, the adaptive fitting weights of each flight element can be obtained, that is, the adaptive fitting weight w h of flight altitude, the adaptive fitting weight w v of environmental wind speed, and the adaptive fitting weight w f of failure flight speed. Thus, the 200 groups of uniform experimental data and the adaptive fitting weights are used in the polynomial fitting algorithm, where the polynomial order is set to 3, the convergence tolerance is 5%, and the maximum number of iterations is 200 times. Thus, the fitting function between the UAV impact load and the three flight elements is obtained, denoted as Q = O(h, v, f), and the maximum impact load of the UAV is obtained, denoted as A max .

[0059] By performing finite element model analysis on the drone, the safe overload value of the drone is obtained. That is, under the safe overload value, the collision damage of the drone will not cause the drone to operate normally. The method for obtaining the safe overload value of the drone is prior art and will not be elaborated in this embodiment. Implementers can obtain it by themselves in actual application scenarios.

[0060] In the design of the drone airbag, the parameters to be designed are: the airbag exhaust hole area x1, the initial inflation pressure x2 of the airbag, and the airbag exhaust threshold x3. Thus, the maximum impact load A of the drone is obtained by using the second-order response surface method. max The response surface fitting function, the specific formula is: In order to further optimize the corresponding response surface model parameters and find a set of airbag design parameter values that minimize the collision impact load of the drone, this embodiment uses a genetic algorithm to optimize the airbag parameters. Among them, an initial population is randomly generated, the population size is set to 150, roulette wheel selection is used for the selection operation, single-point crossover is used for the crossover operation, the crossover coefficient is 0.82, random mutation is used for the mutation operation, the mutation coefficient is 0.1, and the maximum number of iterations is set to 600 times. After iteration, the optimal parameter values of each airbag parameter of the drone are obtained. The genetic optimization algorithm belongs to well-known technology and the specific steps will not be elaborated.

[0061] It should be noted that the response surface fitting function of the maximum impact load of the drone airbag and the optimization process of the airbag parameters are prior art and will not be elaborated in detail in this embodiment. In the actual application process, implementers can select other optimization algorithms for optimization and set them by themselves.

[0062] In this embodiment, through the optimal parameter values corresponding to the optimization by the genetic algorithm, the airbag exhaust hole area x1 of the drone is 742.31 mm2, the initial inflation pressure x2 is 134.68 Kpa, and the exhaust threshold x3 is 270 Kpa. All the obtained optimized parameters are input into the finite element software for calculation, and the impact load value borne by the drone at this time is 11.9 g, which is much smaller than the safe overload value of the drone of 53.4 g and also smaller than the initial value of 27.6 g before optimization. Therefore, the expected optimization effect can be achieved through the above process of this embodiment. The specific results are shown in Table 1.

[0063] Table 1

[0064]

[0065]

[0066] Based on the same inventive concept as the above method, an embodiment of the present application further provides an impact load monitoring system for UAV anti-collision, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above methods for an impact load monitoring method for UAV anti-collision.

[0067] It should be noted that the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0068] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. A method for monitoring impact load for UAV collision avoidance, characterized in that: The method comprises the following steps: The impact load of the UAV in each group of experiments and the data of the three flight elements of the UAV are obtained through intelligent sensors; Based on the data differences of each flight element in all experiments and the gradual change of the impact load of the UAV, the gradual change influence of each flight element is determined, and the calculation formula is: ; In the formula, B represents the gradual influence of the current flight element, N represents the number of data, Indicates the absolute value of the difference between the i-th and i+1-th data values ​​after the data of the current flight element is sorted. It represents the absolute value of the difference between the i-th and i+1-th impact loads after the impact loads of the UAV are sorted. In order to avoid a constant with a denominator of zero, the sorting is to arrange the data values ​​of the current flight elements and the impact load of the UAV in all experiments in ascending order; combining the difference between the data trend distribution of each flight element and the trend distribution of the impact load of the UAV, the synchronous consistency of the impact load of each flight element is obtained, and the calculation formula is: ; Where C represents the synchronous consistency of the impact load of the current flight elements, and They represent the trend strength of the current flight elements and the impact load of the UAV, respectively, where the trend strength is calculated by using the time series decomposition algorithm after sorting; According to the data of each flight element and the impact load data of each UAV, the correlation vector between each flight element and the impact load and the total correlation vector between the three flight elements and the impact load are obtained, the deviation between the correlation vector of each flight element and the total correlation vector is analyzed, and the contribution of each flight element to the impact load is obtained in combination with the synchronization consistency of the impact load; the adaptive fitting weight of each flight element is calculated in combination with the contribution of the three flight elements to the impact load; Based on the adaptive fitting weights of each flight element and combined with the data of each flight element and the impact load data of the UAV, the fitting function between the impact load of the UAV and the three flight elements is fitted to obtain the maximum impact load, and the optimal value of the UAV airbag parameters is obtained using the optimization algorithm.

2. The impact load monitoring method for UAV collision avoidance according to claim 1 is characterized in that: The flight elements include flight altitude, ambient wind speed and failure flight speed.

3. The impact load monitoring method for UAV collision avoidance according to claim 1 is characterized in that: The process of obtaining the correlation vector between each flight element and the impact load and the total correlation vector between the three flight elements and the impact load is as follows: Arrange the data of each flight element in ascending order to obtain the data sequence of each flight element, and arrange the impact load of the UAV in ascending order to form the impact load sequence of the UAV; Each correlation vector between each flight element and the impact load is composed of the elements at the same position in the data sequence of each flight element and the impact load sequence of the UAV; each total correlation vector between the three flight elements and the impact load is composed of the elements at the same position in the data sequence of all flight elements and the impact load sequence of the UAV.

4. The impact load monitoring method for UAV collision avoidance according to claim 1 is characterized in that: The calculation formula for the contribution of each flight element to the impact load is: ; In the formula, Indicates the contribution of the current flight element to the impact load, C indicates the synchronization consistency of the impact load of the current flight element, and N indicates the number of data. represents the angle between the correlation vectors of the i-th and i+1-th current flight elements and the impact load, represents the angle between the total vector associated with the i-th and i+1-th three flight elements and the impact load, To avoid constants with zero denominators.

5. The impact load monitoring method for UAV collision avoidance according to claim 1 is characterized in that: The calculation formula of the adaptive fitting weight of each flight element is: ; In the formula, represents the adaptive fitting weight of the flight altitude, , and They respectively represent the contribution of flight altitude to impact load, the contribution of ambient wind speed to impact load and the contribution of time-dependent flight speed to impact load.

6. The impact load monitoring method for UAV collision avoidance according to claim 1 is characterized in that: The acquisition of the fitting function between the impact load of the UAV and the three flight elements includes: The data of the impact loads of each UAV and each flight element in all groups of experiments are combined with the adaptive fitting weights of each flight element, and a polynomial fitting algorithm is used to fit the fitting function between the UAV impact load and the three flight elements, where the independent variables in the fitting function are the three flight elements, and the dependent variable is the UAV impact load.

7. The impact load monitoring method for UAV collision avoidance according to claim 1 is characterized in that: The acquisition of the optimal value of the drone airbag parameter includes: According to the relationship between the maximum impact load and the design parameters of the UAV airbag, the response fitting function of the maximum impact load of the UAV is obtained, and the optimal values ​​of each airbag parameter of the UAV are obtained using the genetic optimization algorithm.

8. An impact load monitoring system for unmanned aerial vehicle collision avoidance, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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